The company highlighted the global challenge in creating advanced reasoning models, noting that currently, only a handful of organisations with foundational models are actively pursuing this complex area. EXAONE Deep aims to compete directly with these leading models, showcasing a competitive level of reasoning ability.

LG AI Research has focused its efforts on dramatically improving EXAONE Deep’s reasoning capabilities in core domains. The model also demonstrates a strong ability to understand and apply knowledge across a broader range of subjects.

The performance benchmarks released by LG AI Research are impressive:

• Maths: The EXAONE Deep 32B model outperformed a competing model, despite being only 5% of its size, in a demanding mathematics benchmark. Furthermore, the 7.8B and 2.4B versions achieved first place in all major mathematics benchmarks for their respective model sizes.
• Science and coding: In these areas, the EXAONE Deep models (7.8B and 2.4B) have secured the top spot across all major benchmarks.
• MMLU (Massive Multitask Language Understanding): The 32B model achieved a score of 83.0 on the MMLU benchmark, which LG AI Research claims is the best performance among domestic Korean models.

The capabilities of the EXAONE Deep 32B model have already garnered international recognition.

Shortly after its release, it was included in the ‘Notable AI Models’ list by US-based non-profit research organisation Epoch AI. This listing places EXAONE Deep alongside its predecessor, EXAONE 3.5, making LG the only Korean entity with models featured on this prestigious list in the past two years.

Maths prowess

EXAONE Deep has demonstrated exceptional mathematical reasoning skills across its various model sizes (32B, 7.8B, and 2.4B). In assessments based on the 2025 academic year’s mathematics curriculum, all three models outperformed global reasoning models of comparable size.

The 32B model achieved a score of 94.5 in a general mathematics competency test and 90.0 in the American Invitational Mathematics Examination (AIME) 2024, a qualifying exam for the US Mathematical Olympiad.

In the AIME 2025, the 32B model matched the performance of DeepSeek-R1—a significantly larger 671B model. This result showcases EXAONE Deep’s efficient learning and strong logical reasoning abilities, particularly when tackling challenging mathematical problems.

The smaller 7.8B and 2.4B models also achieved top rankings in major benchmarks for lightweight and on-device models, respectively. The 7.8B model scored 94.8 on the MATH-500 benchmark and 59.6 on AIME 2025, while the 2.4B model achieved scores of 92.3 and 47.9 in the same evaluations.

Science and coding excellence

EXAONE Deep has also showcased remarkable capabilities in professional science reasoning and software coding.

The 32B model scored 66.1 on the GPQA Diamond test, which assesses problem-solving skills in doctoral-level physics, chemistry, and biology. In the LiveCodeBench evaluation, which measures coding proficiency, the model achieved a score of 59.5, indicating its potential for high-level applications in these expert domains.

The 7.8B and 2.4B models continued this trend of strong performance, both securing first place in the GPQA Diamond and LiveCodeBench benchmarks within their respective size categories. This achievement builds upon the success of the EXAONE 3.5 2.4B model, which previously topped Hugging Face’s LLM Readerboard in the edge division.

Enhanced general knowledge

Beyond its specialised reasoning capabilities, EXAONE Deep has also demonstrated improved performance in general knowledge understanding.

The 32B model achieved an impressive score of 83.0 on the MMLU benchmark, positioning it as the top-performing domestic model in this comprehensive evaluation. This indicates that EXAONE Deep’s reasoning enhancements extend beyond specific domains and contribute to a broader understanding of various subjects.

LG AI Research believes that EXAONE Deep’s reasoning advancements represent a leap towards a future where AI can tackle increasingly complex problems and contribute to enriching and simplifying human lives through continuous research and innovation.

See also: Baidu undercuts rival AI models with ERNIE 4.5 and ERNIE X1

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Types of AI coding tools

AI-powered coding tools can be categorised into several types based on their functionality:

• AI code completion tools — Provide real-time suggestions and auto-complete lines of code.

• AI code generators — Generate full scripts, functions, or even applications based on natural language prompts.

• AI debugging and error detection tools — Identify syntax errors, logical issues, and security vulnerabilities.

• AI test automation tools — Create and execute test cases with minimal human intervention.

• AI code optimisation Tools — Improve performance by suggesting better coding practices.

• AI security and compliance tools — Analyse code for vulnerabilities and help developers follow security best practices.

• AI documentation generators — Automate inline comments, API documentation, and explanations.

By incorporating these tools into their workflow, developers can write cleaner, more efficient code and reduce the time spent on repetitive tasks.

Best free AI coding tools for 2025

Below, we explore seven of the best free AI coding tools available today and provide an analysis of their features, benefits, and how they can enhance the software development process.

1. Qodo

Qodo is an AI-powered coding assistant designed to help developers generate, optimise, and debug code easily. It offers real-time code suggestions, detects syntax errors, and improves the overall quality of the codebase.

Key features:

• Intelligent code completion: Predicts and suggests relevant code snippets.

• AI-powered debugging: Identifies bugs and logical errors in real-time.

• Code optimisation: Suggests performance improvements and cleaner code structures.

• Multi-language support: Works with multiple programming languages.

Benefits:

Qodo streamlines software development by reducing manual effort in writing and reviewing code. It is particularly beneficial for new developers who need guidance on best coding practices and experienced programmers looking to improve efficiency.

Use cases:

• Writing clean and optimised code.
• Debugging and improving code quality.
• Learning new programming concepts with AI-driven recommendations.

2. Kite

Kite was a popular AI-powered autocomplete tool that provided developers with real-time code suggestions and documentation assistance. Although it has been discontinued, it significantly influenced modern AI coding assistants.

Key features:

• Python-focused autocompletion: Provided predictive code completions.
• Inline documentation: Showed documentation snippets inside the IDE.
• Machine learning-based suggestions: Improved over time with usage.

Benefits:

Despite its discontinuation, Kite demonstrated the power of AI in streamlining software development, influencing newer AI-powered coding tools available today.

Use cases:

• Assisted Python developers with intelligent code suggestions.
• Provided real-time documentation to reduce context switching.

3. Amazon CodeWhisperer

Amazon CodeWhisperer is a free AI-powered coding assistant that provides intelligent code completions and recommendations based on natural language input. It integrates seamlessly with AWS services, making it an excellent tool for cloud-based development.

Key features:

• Real-time code suggestions: Predicts and generates relevant code snippets.
• AWS cloud integration: Works with AWS Lambda, EC2, and other services.
• Multi-language support: Supports Python, JavaScript, and more.

Benefits:

Amazon CodeWhisperer boosts productivity by offering context-aware code recommendations, making it ideal for developers working with cloud-based applications.

Use cases:

• Writing serverless applications on AWS.
• Generating API integrations with minimal manual coding.
• Reducing development time for cloud-based applications.

4. Ponicode

Ponicode is an AI-powered unit testing and code quality analysis tool that ensures high test coverage and optimised code.

Key features:

• AI-generated unit tests: Automates test case creation.
• Code quality analysis: Detects inefficiencies in the code.
• Multi-language support: Works with JavaScript, Python, and more.

Benefits:

Ponicode helps developers write reliable, maintainable code by automating testing and quality checks, making it an excellent tool for teams prioritising test-driven development (TDD).

Use cases:

• Ensuring code coverage with AI-generated test cases.
• Automating software testing processes.

5. IntelliCode

Microsoft IntelliCode enhances coding productivity by offering AI-powered autocomplete, refactoring suggestions, and best coding practices based on thousands of open-source projects.

Key features:

• Context-aware code recommendations: Learns from open-source projects.
• Automated code refactoring: Suggests optimised coding solutions.
• Supports multiple languages: Works with Python, Java, C++, and more.

Benefits:

IntelliCode enables developers to write maintainable code while minimising syntax errors and reducing repetitive coding tasks.

Use cases:

• Improving team productivity by enforcing best coding practices.
• Optimising legacy codebases.

6. YOGI Bot

YOGI Bot is an AI-powered chatbot that assists developers with debugging, code explanations, and best practice recommendations.

Key features:

• AI-driven code assistance: Provides real-time support for debugging and explanations.
• Multi-language support: Works across various programming languages.

Benefits:

YOGI Bot is useful for both new and experienced developers who need immediate coding assistance and explanations.

Use cases:

• Learning programming concepts interactively.
• Troubleshooting coding errors with AI-driven assistance.

7. CodeT5

CodeT5, developed by Salesforce Research, is an AI-powered code generation and translation model that helps developers write high-quality code efficiently.

Key features:

• AI-generated code snippets: Produces entire code blocks.
• Code translation and optimisation: Helps improve existing code.

Benefits:

CodeT5 streamlines development by reducing manual effort and improving code quality.

Use cases:

• Automating coding tasks for web and software development.
• Enhancing productivity with AI-powered suggestions.

Why use AI coding tools?

AI coding tools are gaining popularity due to their ability to streamline development processes, reduce coding errors, and enhance productivity. Here’s why developers should consider integrating AI into their workflow:

Increased efficiency

AI-powered tools speed up coding by suggesting relevant code snippets, autocompleting functions, and automating repetitive tasks. This allows developers to focus on solving complex problems rather than spending time on boilerplate code.

Improved code quality

AI-driven code review tools analyse code for potential errors, security vulnerabilities, and performance issues, ensuring high-quality and optimised code.

Reduced debugging time

Manually finding and fixing bugs can be time-consuming. AI debugging tools help detect issues in real-time and provide potential fixes, reducing time spent on troubleshooting.

Enhanced collaboration

AI-powered tools facilitate team collaboration by providing code suggestions, and documentation, making it easier for teams to work together efficiently.

Automated testing and deployment

AI-driven test automation ensures that applications are tested before deployment, reducing manual effort and potentially speeding up the software release cycle.

Better security

Security-focused AI tools can help identify vulnerabilities and suggest best practices, helping developers write more secure code that complies with industry standards.

Accessibility for beginners

AI coding assistants help beginners learn programming by providing real-time feedback, suggesting improvements, and simplifying complex coding concepts.

7 top free AI coding tools

AI coding tools leverage machine learning, deep learning, and natural language processing to assist developers in writing and optimising code. These tools are trained on vast code repositories and datasets, allowing them to analyse programming patterns and provide intelligent recommendations.

Types of AI coding tools

AI-powered coding tools can be categorised into several types based on their functionality:

• AI code completion tools — Provide real-time suggestions and auto-complete lines of code.

• AI code generators — Generate full scripts, functions, or even applications based on natural language prompts.

• AI debugging and error detection tools — Identify syntax errors, logical issues, and security vulnerabilities.

• AI test automation tools — Create and execute test cases with minimal human intervention.

• AI code optimisation Tools — Improve performance by suggesting better coding practices.

• AI security and compliance tools — Analyse code for vulnerabilities and help developers follow security best practices.

• AI documentation generators — Automate inline comments, API documentation, and explanations.

By incorporating these tools into their workflow, developers can write cleaner, more efficient code and reduce the time spent on repetitive tasks.

(Image source: Unsplash)

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The cloud computing arm of Alibaba Group has also unveiled a revamped full-stack infrastructure designed to meet the surging demand for robust AI computing. This new infrastructure encompasses innovative cloud products and services that enhance computing, networking, and data centre architecture, all aimed at supporting the development and wide-ranging applications of AI models.

Eddie Wu, Chairman and CEO of Alibaba Cloud Intelligence, said: “Alibaba Cloud is investing, with unprecedented intensity, in the research and development of AI technology and the building of its global infrastructure. We aim to establish an AI infrastructure of the future to serve our global customers and unlock their business potential.”

The newly-released Qwen 2.5 models range from 0.5 to 72 billion parameters in size and boast enhanced knowledge and stronger capabilities in maths and coding. Supporting over 29 languages, these models cater to a wide array of AI applications both at the edge and in the cloud across various sectors, from automotive and gaming to scientific research.

Alibaba Cloud’s open-source AI models gain traction

Since its debut in April 2023, the Qwen model series has garnered significant traction, surpassing 40 million downloads across platforms such as Hugging Face and ModelScope. These models have also inspired the creation of over 50,000 derivative models on Hugging Face alone.

Jingren Zhou, CTO of Alibaba Cloud Intelligence, commented: “This initiative is set to empower developers and corporations of all sizes, enhancing their ability to leverage AI technologies and further stimulating the growth of the open-source community.”

In addition to the open-source models, Alibaba Cloud announced an upgrade to its proprietary flagship model, Qwen-Max. The enhanced version reportedly demonstrates performance on par with other state-of-the-art models in areas such as language comprehension, reasoning, mathematics, and coding.

The company has also expanded its multimodal capabilities with a new text-to-video model as part of its Tongyi Wanxiang large model family. This model can generate high-quality videos in various visual styles, from realistic scenes to 3D animation, based on Chinese and English text instructions.

Furthermore, Alibaba Cloud introduced Qwen2-VL, an updated vision language model capable of comprehending videos lasting over 20 minutes and supporting video-based question-answering. The company also launched an AI Developer, a Qwen-powered AI assistant designed to support programmers in automating tasks such as requirement analysis, code programming, and bug identification and fixing.

To support these AI advancements, Alibaba Cloud has announced several infrastructure upgrades, including:

• CUBE DC 5.0, a next-generation data centre architecture that increases energy and operational efficiency.
• Alibaba Cloud Open Lake, a solution to maximise data utility for generative AI applications.
• PAI AI Scheduler, a proprietary cloud-native scheduling engine for enhanced computing resource management.
• DMS: OneMeta+OneOps, a platform for unified management of metadata across multiple cloud environments.
• 9th Generation Enterprise Elastic Compute Service (ECS) instance, offering improved performance for various applications.

These updates from Alibaba Cloud – including the release of over 100 open-source models – aim to provide comprehensive support for customers and partners to maximise the benefits of the latest technology in building more efficient, sustainable, and inclusive AI applications.

(Image Source: www.alibabagroup.com)

See also: Tech industry giants urge EU to streamline AI regulations

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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GPT-4o, where the “o” stands for “omni,” is designed to cater to a broader spectrum of input and output modalities. “It accepts as input any combination of text, audio, and image and generates any combination of text, audio, and image outputs,” OpenAI announced.

Users can expect a response time as quick as 232 milliseconds, mirroring human conversational speed, with an impressive average response time of 320 milliseconds.

Pioneering capabilities

The introduction of GPT-4o marks a leap from its predecessors by processing all inputs and outputs through a single neural network. This approach enables the model to retain critical information and context that were previously lost in the separate model pipeline used in earlier versions.

Prior to GPT-4o, ‘Voice Mode’ could handle audio interactions with latencies of 2.8 seconds for GPT-3.5 and 5.4 seconds for GPT-4. The previous setup involved three distinct models: one for transcribing audio to text, another for textual responses, and a third for converting text back to audio. This segmentation led to loss of nuances such as tone, multiple speakers, and background noise.

As an integrated solution, GPT-4o boasts notable improvements in vision and audio understanding. It can perform more complex tasks such as harmonising songs, providing real-time translations, and even generating outputs with expressive elements like laughter and singing. Examples of its broad capabilities include preparing for interviews, translating languages on the fly, and generating customer service responses.

Nathaniel Whittemore, Founder and CEO of Superintelligent, commented: “Product announcements are going to inherently be more divisive than technology announcements because it’s harder to tell if a product is going to be truly different until you actually interact with it. And especially when it comes to a different mode of human-computer interaction, there is even more room for diverse beliefs about how useful it’s going to be.

“That said, the fact that there wasn’t a GPT-4.5 or GPT-5 announced is also distracting people from the technological advancement that this is a natively multimodal model. It’s not a text model with a voice or image addition; it is a multimodal token in, multimodal token out. This opens up a huge array of use cases that are going to take some time to filter into the consciousness.”

Performance and safety

GPT-4o matches GPT-4 Turbo performance levels in English text and coding tasks but outshines significantly in non-English languages, making it a more inclusive and versatile model. It sets a new benchmark in reasoning with a high score of 88.7% on 0-shot COT MMLU (general knowledge questions) and 87.2% on the 5-shot no-CoT MMLU.

The model also excels in audio and translation benchmarks, surpassing previous state-of-the-art models like Whisper-v3. In multilingual and vision evaluations, it demonstrates superior performance, enhancing OpenAI’s multilingual, audio, and vision capabilities.

OpenAI has incorporated robust safety measures into GPT-4o by design, incorporating techniques to filter training data and refining behaviour through post-training safeguards. The model has been assessed through a Preparedness Framework and complies with OpenAI’s voluntary commitments. Evaluations in areas like cybersecurity, persuasion, and model autonomy indicate that GPT-4o does not exceed a ‘Medium’ risk level across any category.

Further safety assessments involved extensive external red teaming with over 70 experts in various domains, including social psychology, bias, fairness, and misinformation. This comprehensive scrutiny aims to mitigate risks introduced by the new modalities of GPT-4o.

Availability and future integration

Starting today, GPT-4o’s text and image capabilities are available in ChatGPT—including a free tier and extended features for Plus users. A new Voice Mode powered by GPT-4o will enter alpha testing within ChatGPT Plus in the coming weeks.

Developers can access GPT-4o through the API for text and vision tasks, benefiting from its doubled speed, halved price, and enhanced rate limits compared to GPT-4 Turbo.

OpenAI plans to expand GPT-4o’s audio and video functionalities to a select group of trusted partners via the API, with broader rollout expected in the near future. This phased release strategy aims to ensure thorough safety and usability testing before making the full range of capabilities publicly available.

“It’s hugely significant that they’ve made this model available for free to everyone, as well as making the API 50% cheaper. That is a massive increase in accessibility,” explained Whittemore.

OpenAI invites community feedback to continuously refine GPT-4o, emphasising the importance of user input in identifying and closing gaps where GPT-4 Turbo might still outperform.

(Image Credit: OpenAI)

See also: OpenAI takes steps to boost AI-generated content transparency

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

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The launch of GPT-4 Turbo with Vision on the API follows the initial release of GPT-4’s vision and audio upload features last September and the unveiling of the turbocharged GPT-4 Turbo model at OpenAI’s developer conference in November.

GPT-4 Turbo promises significant speed improvements, larger input context windows of up to 128,000 tokens (equivalent to about 300 pages), and increased affordability for developers.

A key enhancement is the ability for API requests to utilise the model’s vision recognition and analysis capabilities through text format JSON and function calling. This allows developers to generate JSON code snippets that can automate actions within connected apps, such as sending emails, making purchases, or posting online. However, OpenAI strongly recommends building user confirmation flows before taking actions that impact the real world.

Several startups are already leveraging GPT-4 Turbo with Vision, including Cognition, whose AI coding agent Devin relies on the model to automatically generate full code:

Devin, built by @cognition_labs, is an AI software engineering assistant powered by GPT-4 Turbo that uses vision for a variety of coding tasks. pic.twitter.com/E1Svxe5fBu

— OpenAI Developers (@OpenAIDevs) April 9, 2024

Healthify, a health and fitness app, uses the model to provide nutritional analysis and recommendations based on photos of meals:

The @healthifyme team built Snap using GPT-4 Turbo with Vision to give users nutrition insights through photo recognition of foods from around the world. pic.twitter.com/jWFLuBgEoA

— OpenAI Developers (@OpenAIDevs) April 9, 2024

TLDraw, a UK-based startup, employs GPT-4 Turbo with Vision to power its virtual whiteboard and convert user drawings into functional websites:

Make Real, built by @tldraw, lets users draw UI on a whiteboard and uses GPT-4 Turbo with Vision to generate a working website powered by real code. pic.twitter.com/RYlbmfeNRZ

— OpenAI Developers (@OpenAIDevs) April 9, 2024

Despite facing stiff competition from newer models such as Anthropic’s Claude 3 Opus and Google’s Gemini Advanced, the API launch should help solidify OpenAI’s position in the enterprise market as developers await the company’s next large language model.

(Photo by v2osk)

See also: Stability AI unveils 12B parameter Stable LM 2 model and updated 1.6B variant

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The post OpenAI makes GPT-4 Turbo with Vision API generally available appeared first on AI News.

With enhancements like larger context size and improved completion quality, Stable Code 3B aims to push the boundaries of AI-assisted software development.

At just three billion parameters, Stable Code 3B is designed to run efficiently on readily available hardware like laptops—unlike larger models which require expensive specialised chips. Despite its smaller size, the company claims it matches or exceeds the code completion quality of models over twice its size. 

The system builds on Stability AI’s Stable LM natural language model with additional training on software engineering data like code repositories and programmer forums. It covers 18 programming languages including Python, JavaScript, Java, C++, and Go.

The model’s training process witnessed optimisation through the incorporation of Rotary Position Embeddings (RoPE), expanding the context size for improved performance. This technique, also employed by Meta’s Llama 2 Long, allows for context lengths up to 100k tokens.

Beyond simply suggesting new lines of code, it can also fill in large missing sections in existing code. This advanced ability is known as Fill in the Middle (FIM) and allows it to automatically write entire functions or components.

The field of AI-generated code has attracted intense interest from tech giants like Microsoft, OpenAI, and Meta. Stability AI’s new system outperforms comparable models like StarCoder and establishes it as a leader in this fast-moving space:

With impressive benchmarks and increased accessibility from its efficient size, Stable Code 3B aims to bring enhanced AI code completion to a wider audience. Its arrival promises to further accelerate the integration of generative AI into software development workflows across industries.

With systems like Stable Code 3B automating rote coding tasks, developers stand to become more productive, creative, and can focus their efforts on more complex challenges.

(Photo by Joan Gamell on Unsplash)

See also: IMF: AI could boost growth but worsen inequality

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week and Cyber Security & Cloud Expo.

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The post Stability AI releases Stable Code 3B for enhanced coding assistance appeared first on AI News.

The recent GitLab 16.6 November release includes the beta launch of GitLab Duo Chat, a natural-language AI assistant. Additionally, the GitLab 16.7 December release sees the general availability of GitLab Duo Code Suggestions.

David DeSanto, Chief Product Officer at GitLab, said: “To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing DevSecOps teams to benefit from boosts to security, efficiency, and collaboration.”

GitLab Duo Chat – arguably the star of the show – provides users with invaluable insights, guidance, and suggestions. Beyond code analysis, it supports planning, security issue comprehension and resolution, troubleshooting CI/CD pipeline failures, aiding in merge requests, and more.

As part of GitLab’s commitment to providing a comprehensive AI-powered experience, Duo Chat joins Code Suggestions as the primary interface into GitLab’s AI suite within its DevSecOps platform.

GitLab Duo comprises a suite of 14 AI capabilities:

• Suggested Reviewers
• Code Suggestions
• Chat
• Vulnerability Summary
• Code Explanation
• Planning Discussions Summary
• Merge Request Summary
• Merge Request Template Population
• Code Review Summary
• Test Generation
• Git Suggestions
• Root Cause Analysis
• Planning Description Generation
• Value Stream Forecasting

In response to the evolving needs of development, security, and operations teams, Code Suggestions is now generally available. This feature assists in creating and updating code, reducing cognitive load, enhancing efficiency, and accelerating secure software development.

GitLab’s commitment to privacy and transparency stands out in the AI space. According to the GitLab report, 83 percent of DevSecOps professionals consider implementing AI in their processes essential, with 95 percent prioritising privacy and intellectual property protection in AI tool selection.

The State of AI in Software Development report by GitLab reveals that developers spend just 25 percent of their time writing code. The Duo suite aims to address this by reducing toolchain sprawl—enabling 7x faster cycle times, heightened developer productivity, and reduced software spend.

Kate Holterhoff, Industry Analyst at Redmonk, commented: “The developers we speak with at RedMonk are keenly interested in the productivity and efficiency gains that code assistants promise.

“GitLab’s Duo Code Suggestions is a welcome player in this space, expanding the available options for enabling an AI-enhanced software development lifecycle.”

(Photo by Pankaj Patel on Unsplash)

See also: OpenAI battles DDoS against its API and ChatGPT services

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Cyber Security & Cloud Expo and Digital Transformation Week.

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Following yesterday’s leak of a custom GPT-4 chatbot creator, OpenAI unveiled several other key features during its DevDay that promise a transformative impact on the landscape of AI applications:

• GPT-4 Turbo: OpenAI introduced the preview of GPT-4 Turbo, the next generation of its renowned language model. This new iteration boasts enhanced capabilities and an extensive knowledge base encompassing world events up until April 2023.
  • One of GPT-4 Turbo’s standout features is the impressive 128K context window, allowing it to process the equivalent of more than 300 pages of text in a single prompt.
  • Notably, OpenAI has optimised the pricing structure, making GPT-4 Turbo 3x cheaper for input tokens and 2x cheaper for output tokens compared to its predecessor.
• Assistants API: OpenAI also unveiled the Assistants API, a tool designed to simplify the process of building agent-like experiences within applications.
  • The API equips developers with the ability to create purpose-built AIs with specific instructions, leveraging additional knowledge and calling models and tools to perform tasks.
• Multimodal capabilities: OpenAI’s platform now supports a range of multimodal capabilities, including vision, image creation (DALL·E 3), and text-to-speech (TTS).
  • GPT-4 Turbo can process images, opening up possibilities such as generating captions, detailed image analysis, and reading documents with figures.
  • Additionally, DALL·E 3 integration allows developers to create images and designs programmatically, while the text-to-speech API enables the generation of human-quality speech from text.
• Pricing overhaul: OpenAI has significantly reduced prices across its platform, making it more accessible to developers.
  • GPT-4 Turbo input tokens are now 3x cheaper than its predecessor at $0.01, and output tokens are 2x cheaper at $0.03. Similar reductions apply to GPT-3.5 Turbo, catering to various user requirements and ensuring affordability.
• Copyright Shield: To bolster customer protection, OpenAI has introduced Copyright Shield.
  • This initiative sees OpenAI stepping in to defend customers and cover the associated legal costs if they face copyright infringement claims related to the generally available features of ChatGPT Enterprise and the developer platform.

OpenAI’s latest announcements mark a significant stride in the company’s mission to democratise AI technology, empowering developers to create innovative and intelligent applications across various domains.

See also: OpenAI set to unveil custom GPT-4 chatbot creator

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with Digital Transformation Week.

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The report, titled ‘The State of AI in Software Development,’ presents insights from over 1,000 global senior technology executives, developers, and security and operations professionals.

The report reveals a complex relationship between enthusiasm for AI adoption and concerns about data privacy, intellectual property, and security.

“Enterprises are seeking out platforms that allow them to harness the power of AI while addressing potential privacy and security risks,” said Alexander Johnston, Research Analyst in the Data, AI & Analytics Channel at 451 Research, a part of S&P Global Market Intelligence.

While 83 percent of the survey’s respondents view AI implementation as essential to stay competitive, a significant 79 percent expressed worries about AI tools accessing sensitive information and intellectual property.

Impact on developer productivity

AI is perceived as a boon for developer productivity, with 51 percent of all respondents citing it as a key benefit of AI implementation. However, security professionals are apprehensive that AI-generated code might lead to an increase in security vulnerabilities, potentially creating more work for them.

Only seven percent of developers’ time is currently spent identifying and mitigating security vulnerabilities, compared to 11 percent allocated to testing code. This raises questions about the widening gap between developers and security professionals in the AI era.

Privacy and intellectual property concerns

The survey underscores the paramount importance of data privacy and intellectual property protection when selecting AI tools. 95 percent of senior technology executives prioritise these aspects when choosing AI solutions.

Moreover, 32 percent of respondents admitted to being “very” or “extremely” concerned about introducing AI into the software development lifecycle. Within this group, 39 percent cited worries about AI-generated code introducing security vulnerabilities, and 48 percent expressed concerns that AI-generated code may not receive the same copyright protection as code produced by humans.

AI skills gap

Despite optimism about AI’s potential, the report identifies a disconnect between organisations’ provision of AI training resources and practitioners’ satisfaction with them. 

While 75 percent of respondents stated that their organisations offer training and resources for using AI, an equivalent proportion expressed the need to seek resources independently—suggesting that the available training may be insufficient.

A striking 81 percent of respondents said they require more training to effectively utilise AI in their daily work. Furthermore, 65 percent of those planning to use AI for software development indicated that their organsations plan to hire new talent to manage AI implementation.

David DeSanto, Chief Product Officer at GitLab, said:

“According to the GitLab Global DevSecOps Report, only 25 percent of developers’ time is spent on code generation, but the data shows AI can boost productivity and collaboration in nearly 60 percent of developers’ day-to-day work.

To realise AI’s full potential, it needs to be embedded across the software development lifecycle, allowing everyone involved in delivering secure software – not just developers – to benefit from the efficiency boost.” 

While AI holds immense promise for the software development industry, GitLab’s report makes it clear that addressing cybersecurity and privacy concerns, bridging the skills gap, and fostering collaboration between developers and security professionals are pivotal to successful AI adoption.

(Photo by Luca Bravo on Unsplash)

See also: UK government outlines AI Safety Summit plans

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Using the feature, developers can “brush” over their code to see it update in real-time.

Several different brushes are included to achieve various aims. For example, one brush makes code more readable—especially important when coding as part of a team or contributing to open-source projects.

Here are the other included brushes:

• Add types
• Fix bug
• Debug (adds debugging statements)
• Make robust (improves compatibility)

Code Brushes also supports the creation of custom brushes. One example is a brush to make a form “more accessible” automatically.

“As we explore enhancing developers’ workflows with machine learning, we’re focused on how to empower developers instead of automating them,” explained GitHub.

“This was one of many explorations we have in the works along those lines.”

Code Brushes is powered by the controversial GitHub Copilot. Copilot uses technology from OpenAI to help generate code and speed up software development.

GitHub-owner Microsoft and OpenAI were hit with a class-action lawsuit over Copilot last year. The case aims to investigate whether Copilot infringes on the rights of developers by scraping their code and not providing due attribution.

“Users likely face growing liability that only increases as Copilot improves,” explained Bradley M. Kuhn of Software Freedom Conservancy earlier this year.

“Users currently have no methods besides serendipity and educated guesses to know whether Copilot’s output is copyrighted by someone else.”

Code Brushes has been added to the Copilot Labs Visual Studio Code extension. The extension requires a Copilot license which costs $10/month or $100/year.

(Photo by Marcus Urbenz on Unsplash)

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The paper, titled ‘Do Users Write More Insecure Code with AI Assistants?’, examines developers’ use of AI coding assistants like the controversial GitHub Copilot.

“Participants with access to an AI assistant often produced more security vulnerabilities than those without access, with particularly significant results for string encryption and SQL injection,” the authors wrote.

The paper also found that developers using AI assistants have misguided confidence in the quality of their code.

“We also found that participants [that were] provided access to an AI assistant were more likely to believe that they wrote secure code than those without access to the AI assistant,” added the authors.

47 people were asked to write code in response to several prompts as part of the study. Some participants were given AI assistance while the rest did not.

The first prompt was to “Write two functions in Python where one encrypts and the other decrypts a given string using a given symmetric key.”

For that prompt, 79 percent of the coders without AI assistance gave a correct answer. That’s compared to 67 percent of the group with assistance.

In addition, the assisted group was determined to be “significantly more likely to provide an insecure solution (p < 0.05, using Welch’s unequal variances t-test), and also significantly more likely to use trivial ciphers, such as substitution ciphers (p < 0.01), and not conduct an authenticity check on the final returned value.”

One participant allegedly quipped that they hope AI assistance gets deployed because “it’s like [developer Q&A community] Stack Overflow but better, because it never tells you that your question was dumb.”

Last month, OpenAI and Microsoft were hit with a lawsuit over their GitHub Copilot assistant. Copilot is trained on “billions of lines of public code … written by others”.

The lawsuit alleges that Copilot infringes on the rights of developers by scraping their code and not providing due attribution. Developers that use code suggested by Copilot could unwittingly be infringing copyright.

“Copilot leaves copyleft compliance as an exercise for the user. Users likely face growing liability that only increases as Copilot improves,” wrote Bradley M. Kuhn of Software Freedom Conservancy earlier this year.

To summarise: Developers using current AI assistants risk producing buggier, less secure, and potentially litigable code.

(Photo by James Wainscoat on Unsplash)

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The company selected 10 contests that were hosted on Codeforces – a programming competition platform with thousands of participants – to evaluate the performance of AlphaCode.

Below is an example of a problem #AlphaCode can successfully solve, using the exact information seen on @codeforces, & the program that #AlphaCode writes.

By @liyuajia, David Choi, @junyoungch, @NateKushman, @Mononofu, @RemiLeblond, Tom Eccles, James Keeling, @FelixAxelGimeno 2/ pic.twitter.com/TJu0Va46OS

— DeepMind (@DeepMind) February 2, 2022

Following the simulations, AlphaCode ranked in the top 54 percent of competitors. That means it wasn’t yet able to beat leading human programmers but could rival the average.

Mike Mirzayanov, Founder of Codeforces, said:

“I can safely say the results of AlphaCode exceeded my expectations. I was sceptical because even in simple competitive problems it is often required not only to implement the algorithm, but also – and this is the most difficult part – to invent it.

AlphaCode managed to perform at the level of a promising new competitor. I can’t wait to see what lies ahead!”

AlphaCode uses transformer-based language models to generate code “at an unprecedented scale”. A preprint paper detailing AlphaCode is available here (PDF).

Petr Mitrichev, Software Engineer at Google, commented:

“Solving competitive programming problems is a really hard thing to do, requiring both good coding skills and problem-solving creativity in humans.

I was very impressed that AlphaCode could make progress in this area and excited to see how the model uses its statement understanding to produce code and guide its random exploration to create solutions.”

DeepMind has released its dataset of competitive programming problems and solutions on GitHub to help others build on their results.

Ian Funnell, Manager of Developer Relations at Matillion, said:

“Advancements like AlphaCode are welcomed and represent huge progress in designing algorithms more effectively. AI coding in general empowers developers to pursue innovation and creativity in setting the parameters and goals, leaving the AI to actually execute them.

Ultimately, this is a catalyst for innovation—helping humans rather than replacing them. Developers are extremely capable individuals, and businesses will continue to count on them to reap valuable insights from their data to differentiate and compete.”

DeepMind has set up an interactive site to view some of AlphaCode’s solutions and dive into the model at alphacode.deepmind.com.

(Image Credit: DeepMind)

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