AI Trends in 2025

Welcome to AI Trends in 2025 from Courses Buddy!

Artificial intelligence is rapidly transforming both our personal and professional lives, evolving at a pace that’s challenging to keep up with. We are witnessing a new industrial revolution, making it more crucial than ever to stay updated on the AI tools and technologies shaping the future of work. 

In this guide, we will share key insights into the latest AI trends and innovations, helping you stay competitive in an ever-changing landscape.

Welcome to AI trends!

OpenAI released ChatGPT 4.5, a model that excels particularly in one area—persuasion. Much of our work involves analysing data, preparing reports, and making predictions, often with the goal of convincing others. Interestingly, 4.5 has stirred controversy, as it doesn’t perform well on existing tests and is costly to run.

Sam Altman has stated that 4.5 is the last non-chain-of-thought model, with future versions focusing on reasoning at scale. While it reduces hallucinations and offers deeper knowledge, it remains expensive—costing $93 per million tokens compared to Gemini Flash’s $0.20.

Despite its high cost, 4.5 is particularly strong in persuasion. OpenAI evaluated its ability to manipulate other models, where it outperformed most competitors, except Deep Research. In deception tests, it achieved a remarkable 72% success rate. Due to potential risks, OpenAI has implemented safeguards against political manipulation, cybersecurity threats, and unethical use.

Unlike previous OpenAI models that dominated across tasks, 4.5’s strengths are more nuanced. It’s a superior writing and persuasion tool but less effective for development or reasoning-heavy tasks. With ChatGPT 5.0 on the horizon, OpenAI aims to build on these learnings, creating a more powerful and adaptable model.

AI has dramatically transformed the role of programmers in recent years. Developers using AI complete 26% more work, finish coding tasks 55% faster, and 81% believe increased productivity is the biggest advantage. Even if you’ve tried AI coding tools before, the landscape is evolving rapidly, with new models now outperforming top developers in coding competitions.

This new wave of AI models can grasp the entire context of your codebase, assisting with debugging, commenting, and refactoring. Acting as an AI pair programmer, they reduce the need for constant online searches.

Cursor, a modified version of Visual Studio Code, is a popular choice among developers. It allows custom AI integration, supporting models like Claude, DeepSeek, and OpenAI’s o3. By understanding all code within a project, Cursor offers precise suggestions, whether modifying existing code or generating app templates from scratch. It also simplifies debugging, feature control, and terminal commands, enabling developers to build powerful applications in minutes rather than hours.

ChatGPT’s Operator is an advanced AI agent designed to automate tasks by interacting with a browser, mouse, and keyboard. However, it’s not always the best tool for every situation. Let’s explore its requirements and key considerations before use.

Operator is currently available to select OpenAI accounts, requiring a ChatGPT Pro subscription at $200 per month. It is expected to roll out to more affordable plans over time but is not yet accessible for business accounts like Teams or Enterprise. As a research preview, it is still in an early stage, meaning it may not be suitable for widespread business adoption just yet.

Following OpenAI’s usual rollout pattern, Operator is likely to become available to the $20/month Plus plan soon but is unlikely to reach the free tier due to its high operational costs. If eligible, users can find Operator in the ChatGPT sidebar or visit its standalone site at operator.chatgpt.com.

The interface resembles ChatGPT, featuring an input field, file upload options, suggested tasks, and a history panel for managing past instructions. Operator can interact with web elements such as input fields, forms, menus, and buttons, with future expansions planned for other applications.

By combining ChatGPT with additional programming, Operator can self-correct errors, pause for manual logins, and store account information for recurring tasks. Users can also create custom instructions for specific websites, making repetitive tasks more efficient.

While similar automation tools exist, Operator is one of the first commercially available AI-powered agents. As 2025 is set to be dominated by autonomous AI tools, it will be fascinating to see how Operator evolves and adapts for various use cases.

ChatGPT offers various AI models, each with distinct capabilities. Understanding these differences is crucial for selecting the best model for your project.

ChatGPT Account Types and Access

Your account type significantly affects the models and features available to you:

1. Free users (no account): Access to ChatGPT-4o Mini with a 5 KB context window (roughly five pages of text). However, file uploads, image analysis, and advanced features such as search, projects, or canvas are unavailable.
2. Free users (with an account): Gain access to ChatGPT, search, and canvas mode, along with limited image generation. However, projects and custom GPTs remain locked.
3. Paid users: Upgrading to a ChatGPT Plus or Pro account unlocks better models, larger context windows, and additional functionalities.

New features often roll out to paid accounts first, and some may eventually become available to free users, but this isn’t guaranteed.

Comparing AI Models

One useful resource for evaluating AI models is the Artificial Analysis website, which provides a detailed comparison of model quality, speed, and cost.

1. If you need AI for answering questions, model quality is the most important factor.
2. If you’re building an application, speed and cost become more relevant.

Paid plans offer increased rate limits, meaning you can ask more questions before reaching usage caps.

Understanding OpenAI’s Models

OpenAI provides several models, including:

1. GPT-4o (default for paid users): High-quality responses with strong general performance.
2. O-series models (e.g., o1, o3): Designed for advanced reasoning, offering step-by-step problem-solving and the ability to integrate web search data for more informed answers.

Unlike traditional models, the O-series spends extra time planning responses before generating them. This structured approach is particularly beneficial for developers tackling complex coding challenges.

What’s New with O3?

O3, introduced during the “12 Days of Shipmas” in 2024, takes this reasoning-based approach further with program synthesis, dynamically combining algorithms to solve unfamiliar tasks. While it offers better functionality, it comes at a cost—requiring a ChatGPT Pro account ($200/month) for access to its enhanced compute capabilities.

Choosing the Right Model for Your Needs

1. If you need quick responses for simple tasks, GPT-4o is sufficient.
2. If you require deeper analysis and problem-solving, O-series models (e.g., o1 or o3) are ideal.
3. If you are a developer working on complex AI-driven applications, investing in a Pro account for advanced models and extended rate limits may be worthwhile.

Ultimately, the right model depends on your specific use case, budget, and required performance.

Gemini 2.0 is Google’s latest foundational generative AI model, designed for multimodal tasks, meaning it can process text, code, images, video, and spatial reasoning natively. It has demonstrated improved performance across key AI benchmarks, particularly in reasoning and multimodal capabilities, leading to more accurate results in object identification, captioning, and complex scene understanding.

Key Features and Capabilities

1. Multimodal AI – Gemini 2.0 is built to handle text, images, video, and spatial data seamlessly.
2. Advanced Reasoning – Improved logic and problem-solving, with better grounding through Google Search and customizable source data.
3. AI for the Agentic Era – Designed to integrate with tools like Google Search and code execution, making responses more factual and context-aware.
4. Model Variants –
   • Gemini 2.0 Flash: A faster and more efficient version optimized for quick responses and wider availability.
5. Huge Context Windows – Allows for longer, more sophisticated interactions and memory retention.
6. Customizability & Fine-Tuning – Developers can tailor Gemini 2.0 for specific applications and domains.

Best Use Cases

Gemini 2.0 is ideal for projects requiring:
✅ Complex multimodal interactions (e.g., AI-powered design tools)
✅ Advanced reasoning and problem-solving (e.g., research assistants)
✅ Integration with external tools and APIs
✅ Creative content generation (e.g., interactive storytelling, personalized education, customer service chatbots)

Access & Deployment

1. Google AI Studio – Ideal for experimentation and prototyping.
2. Google Cloud Vertex AI Notebooks – For scaling and deploying Gemini-powered applications in production.
3. Gemini API – Enables developers to integrate Gemini 2.0 into custom applications.

Currently, Gemini 2.0 Flash is available with multimodal input and text output, alongside features like text-to-speech and native image generation.

Future Updates

Google plans to release updated Gemini 2.0 courses in 2025, offering deeper insights into its capabilities and applications.

In late January 2025, the AI landscape witnessed a seismic shift when DeepSeek-R1 catapulted the DeepSeek app to the number one spot on U.S. app stores. This unexpected surge even caused a temporary dip in global tech stocks, signaling a major shakeup in the industry. But what exactly is DeepSeek-R1, and why does it matter?

What is DeepSeek-R1?

DeepSeek-R1 and its counterpart, R1-Zero, are groundbreaking AI reasoning models developed by the Chinese company DeepSeek. Prior to their release, OpenAI’s o1 models were the only established reasoning models available. DeepSeek-R1 debuted on January 10, 2025, via the free DeepSeek AI chatbot app, followed by an open-source release of the full model and weights just ten days later. By January 27, the DeepSeek app had surpassed ChatGPT as the most downloaded free app in the U.S.

What makes DeepSeek-R1 stand out is its cost-efficiency. While OpenAI reportedly spent $100 million developing GPT-4 in 2023, DeepSeek managed to create R1 with just $6 million—an astonishing reduction. Early benchmarks indicate R1 may rival or even surpass GPT-4o and Claude Opus 3.5, bringing DeepSeek to the forefront of the AI race.

Why Does DeepSeek Matter?

DeepSeek’s models are fully open-source, allowing anyone to download, modify, and deploy them for free—unlike those from OpenAI or Anthropic. Furthermore, R1-Zero models are compact and efficient enough to run directly on smartphones and IoT devices, enabling offline AI reasoning.

But the real game-changer lies in DeepSeek’s innovative training methods. By leveraging existing models to generate training data, the company has drastically cut down hardware requirements, time, and energy consumption. This approach challenges the long-held belief that AI development requires massive computational power, expensive hardware, and substantial financial investment.

Surprisingly, DeepSeek achieved this breakthrough despite facing technological constraints due to U.S. chip bans and limited access to cutting-edge processors. This development suggests that AI advancement is not solely dependent on expensive infrastructure but also on innovative methodologies.

Should You Switch to DeepSeek?

Despite its promise, DeepSeek is not without its caveats:

1. Censorship Risks – As a Chinese company, DeepSeek operates under government-imposed content restrictions. Sensitive topics, such as the Tiananmen Square Massacre, will yield state-approved responses or refusals to generate content.
2. Data Privacy Concerns – The app collects user input, device information, and IP addresses, storing them on servers in China. Unlike companies in the U.S. and Europe, where regulations ensure some level of data oversight, DeepSeek’s data practices lack external transparency.
3. New and Untested – DeepSeek-R1 is still in its infancy. Businesses and enterprises must conduct rigorous testing before considering it for professional use.

The Future of AI with DeepSeek

While DeepSeek-R1 may not yet be a perfect replacement for existing AI tools, its disruptive approach has already reshaped industry dynamics. The AI sector will likely see a wave of new models inspired by DeepSeek’s cost-effective and efficient training strategies. Whether DeepSeek itself remains a dominant player is uncertain, but its impact is undeniable.

As AI continues to evolve at breakneck speed, one thing remains clear—staying ahead in this field requires agility, adaptability, and a keen eye on the latest innovations. DeepSeek has opened new doors, and the world is now watching to see what comes next.

OpenAI’s Canvas introduces an innovative approach to working with ChatGPT, revolutionising how users interact with AI to create and refine documents. Unlike traditional prompting, Canvas provides an interactive interface, offering a more dynamic and efficient way to edit and format content.

Enhancing the Writing Experience

One of the key features of Canvas is its ability to refine and modify text seamlessly. Users can select specific portions of text and request changes, such as expanding an introduction or simplifying complex sentences. By clicking the plus button next to each paragraph, users can prompt the AI to make adjustments, ensuring the output aligns perfectly with their needs.

Interactive Editing and Formatting

Canvas allows real-time modifications with intuitive tools. When selecting text, an edit bar appears, enabling users to apply formatting options such as bold, italics, or heading adjustments. Additionally, users can delete sentences, request a more detailed explanation, or even shorten lengthy paragraphs with a single click.

Version Control and Refinement

Users can navigate through different versions of their content with the arrow buttons. This functionality allows for easy comparison of changes, and they can choose to either restore a previous version or set the latest one as the official edit. Furthermore, a timeline feature highlights modifications, helping users track adjustments effortlessly.

Adjusting Content for Readability

Canvas provides flexibility in adjusting content length and readability levels. Users can specify paragraph lengths, ensuring concise yet informative content. Additionally, reading levels can be modified to suit different audiences, from middle school to graduate-level proficiency. A ‘Final Polish’ option is also available to refine grammar and consistency, ensuring a professional finish.

Additional Features

For added customisation, Canvas allows users to insert emojis into the text. However, if deemed unnecessary, previous versions can be restored without them. Once satisfied with the final draft, users can close the Canvas interface while still being able to reopen and make further refinements when needed.

The Future of AI-Powered Writing

OpenAI Canvas offers an intuitive and powerful way to interact with AI-driven content creation. The ability to make precise edits, adjust readability, and refine formatting makes it a game-changer in AI-assisted writing. This interactive approach to prompting represents the future of AI-driven content generation, providing users with unparalleled control over their documents.

For more insights and access to OpenAI’s tools, visit plAItime.com and explore the README Generator, where you can find detailed information on its prompt structure and usage.

Anthropic’s agentic computer use is a key feature of the new Claude 3.5 Sonnet model. It enables users to command an AI to operate computers much like a human would.

How Does It Work?

Claude follows a structured approach to executing tasks:

1. Breaking Down Prompts into Steps – It analyses the user’s request and formulates a step-by-step plan.
2. Taking Screenshots for Context – Claude captures an initial screenshot to understand the current screen.
3. Executing Actions – The AI attempts to perform tasks by issuing mouse clicks, keystrokes, and commands.

Example Task

For instance, if a user asks Claude to visit a website, extract information about three custom GPTs (title, summary, link, date), and create a spreadsheet, the AI will:

1. Open a web browser (e.g., Firefox).
2. Navigate to the specified website.
3. Extract the required data.
4. Organise the information into a spreadsheet.

Throughout the process, Claude continuously takes screenshots and issues commands until the task is completed.

Capabilities and Applications

Claude can operate various applications, including:

1. Web browsers (for data extraction).
2. Terminal (for executing scripts and commands).
3. Calculator (for performing computations).
4. Spreadsheets (for organising and storing data).

These functionalities allow users to automate research, programming tasks, and other operations that require complex keyboard and mouse interactions.

Challenges and Limitations

Claude is still under development, which means users might face occasional issues. However, its agentive nature allows it to troubleshoot and attempt to resolve errors autonomously.

Some notable limitations include:

1. API Rate Limits – Free-tier users may encounter restrictions on the number of requests they can make.
2. Token Consumption – Tasks like taking screenshots can quickly consume API tokens.

Managing API Usage

Users facing API limits can:

1. Visit the developer console.
2. Navigate to Settings → Billing.
3. Add funds to increase usage capacity.

Although still in its early stages, agentic computer use in Claude 3.5 Sonnet represents a significant step towards AI-driven automation. By handling complex interactions across multiple applications, Claude provides a glimpse into the future of intelligent task automation.

OpenAI’s o1 model, also known as Strawberry, is a new AI model designed to slow down and mimic human reasoning before generating responses. Unlike other AI models that provide instant answers, o1 carefully processes the input, identifies key information, and then delivers more data-driven and less generic responses.

How OpenAI o1 Works

When you input a query, o1 follows a structured reasoning process:

1. Identifying Key Data – The model scans the provided information.
2. Recognizing Structure – It organises the data for analysis.
3. Confirming Facts – It verifies stock prices, figures, or other relevant details.
4. Generating Insights – Finally, it formulates an answer based on logical reasoning.

This step-by-step approach appears visually as a “Thinking” process with a gray line beside it, indicating ongoing analysis.

Example Use Case: Market Analysis

In an experiment, a user provided S&P 500 index data and asked:
“What can you tell me about the market today based on this data?”

o1 processed the input, structured the information, and responded with specific insights, such as:

• Technology Sector Growth – Strong performance from major tech companies.
• Market Trends – Insights based on real-time data rather than generic summaries.

This approach sets o1 apart from models that provide generalised or overly broad answers.

Key Advantages

1. Less Generic Responses – Answers are rooted in the input data, making them more reliable.
2. Reduced Hallucinations – Fewer inaccuracies compared to other AI models.
3. Better for Data Analysis – Excels in processing structured information like financial data, reports, and research materials.

Limitations

1. Slower Performance – Due to its reasoning-based approach, o1 takes longer to generate responses.
2. Higher Cost – It is significantly more expensive to run compared to models like GPT-4o or Gemini.
3. Not Ideal for Quick Tasks – Tasks like writing a short email or a social media post are better suited for faster models.

OpenAI’s o1 brings generative AI capabilities into areas previously beyond its reach, such as advanced research and business problem-solving. While it is not a replacement for fast-processing AI models like GPT-4o or Gemini, its human-like reasoning makes it ideal for tasks that require deep analysis and data-driven insights.

GitHub has introduced GitHub Models, a groundbreaking service that simplifies AI prototyping and development. Unlike typical AI marketing hype, this platform genuinely transforms the landscape by providing direct access to multiple AI models from different vendors, all within GitHub’s ecosystem.

What is GitHub Models?

GitHub Models is a multi-vendor model marketplace available at github.com/marketplace/models. It offers access to:

1. Large Language Models (LLMs)
2. Small Language Models (SLMs)
3. Embedding Models

These models come from top AI providers, including:
✅ Mistral
✅ OpenAI
✅ Llama
✅ Phi

You can explore each model’s model card, test them in a playground, tweak their settings, and even switch between vendors to compare performance.

How It Works

1. Browse and Select a Model – View different models and their specifications.
2. Test in the Playground – Interact with the model and adjust properties.
3. Switch Between Models – Easily compare outputs from different providers.
4. Copy Code for Integration – Click Get Started to generate ready-to-use code snippets.

At first glance, this may seem like just another multi-vendor AI model marketplace, but three key features set it apart.

Why GitHub Models is a Game Changer

1. Free Access for All GitHub Users

✅ No separate accounts required – Your GitHub account is all you need.
✅ Even free GitHub accounts get 50 requests per day – Sufficient for substantial prototyping.
✅ No upfront cost – Ideal for experimentation before scaling up.

2. Seamless Integration with Azure AI

GitHub Models is powered by Azure AI, allowing effortless integration:
🔹 Single Endpoint for All Models – Swap between models without modifying code.
🔹 Azure AI SDK Support – Implement models with one authentication step.
🔹 Quick Deployment – Host your AI apps on Azure AI with minimal setup.

3. No Authentication Hassles

This is where GitHub Models truly redefines AI development:
🟢 No API keys or manual authentication – Your GitHub account handles everything.
🟢 Works Instantly in GitHub Codespaces – Simply open a repository, and AI models are ready to use.
🟢 Seamless Local Editor Integration – If you have authenticated VS Code or another editor with GitHub, no additional setup is required.
🟢 Easy Vendor Switching – If you want to use a different AI provider, simply swap out the endpoint and authenticate with vendor keys.

Lowering the Barrier to AI Development

GitHub Models makes AI prototyping as straightforward as possible:
✔️ No extra accounts
✔️ No complex authentication
✔️ No vendor lock-in

Anyone with a GitHub account can start working with LLMs, SLMs, and embeddings immediately—whether in Codespaces, VS Code, or any preferred environment.

GitHub Models democratises AI development by removing barriers, making it easier for developers to experiment, compare models, and build AI applications. It’s not just a new tool—it’s a new standard for AI prototyping.

The future of AI development just became much more accessible!

Claude Artifacts introduces an innovative and transformational way to create interactive tools. It sets a new standard for how humans interact with AI through their prompts. There’s been considerable buzz around this product, so let’s explore its core features and how it works.

To test its capabilities, I had Claude build a game to help me learn technical concepts. My prompt was simple:

“Create a flashcard game to help me learn Git concepts.”

As soon as I submitted the prompt, Claude began generating the code. Once finished, it opened a preview window of the game, choosing JavaScript and React as the programming language and framework. Impressively, it also provided a clear explanation of what it had done.

How Claude Artifacts Works

The flashcard game allowed me to see the answers, mark them as correct or incorrect, and functioned as a traditional learning tool. And the most impressive part? It worked on the first try with a simple prompt.

You may have seen numerous examples online of Claude Artifacts generating impressive results on the first attempt. However, the reality is more nuanced:
✔ Sometimes, it produces something great.
✔ Other times, it fails or delivers unexpected results.

Claude Artifacts allows you to switch between code and preview, refresh the game, and even download the code for local use. You can also publish your Artifact, making it accessible to others.

Refining and Iterating with Claude

One of the standout features of Claude Artifacts is its interactive feedback loop. You can refine the output by having a back-and-forth conversation with Claude.

For instance, I asked:

“I’d like to move through the questions with buttons and reshuffle the cards for another attempt.”

Claude then modified the game to align more closely with my vision. Additionally, it introduced a version history, allowing me to track the evolution of my Artifact.

Building a Breakout Game with Claude

To push the tool further, I tried another experiment:

“Can you write me a Breakout game? Make it colourful.”

Claude generated a playable Breakout game—not perfect on the first attempt, but functional. Through incremental refinements, I asked it to:

1. Improve collision detection
2. Add scoring
3. Ensure all blocks were visible
4. Enable mouse controls

Each revision brought notable improvements. However, my experience highlighted an important reality: Claude is not always consistent. On a later attempt, it either refused to generate the game or produced broken code.

Despite occasional inconsistencies, Claude Artifacts is incredibly promising. With the right prompts and a bit of patience, it can generate highly useful interactive tools today. I am excited to see how Anthropic continues to develop this feature.

Claude Artifacts is redefining how we build AI-powered applications—one prompt at a time.

On May 21st, the Microsoft Build Developers Conference kicked off with a wave of announcements, primarily focused on Copilot and AI development. However, even before the event, Microsoft introduced a new class of AI-powered computers designed for on-device AI processing—Copilot+ PCs.

Copilot+ PCs: An Era of AI-Powered Computing

Traditionally, computers rely on CPUs, GPUs, and RAM, but these new Copilot+ PCs introduce a Neural Processing Unit (NPU), specifically built to handle AI workloads locally. On-device AI processing significantly enhances speed, enables offline functionality, and improves privacy by keeping sensitive data on the device rather than sending it to cloud servers.

Microsoft will sell its own Copilot+ PC devices while also providing specifications for third-party manufacturers. These devices will be available starting June 18.

New AI-Powered Features in Windows

Alongside Copilot+ PCs, Microsoft introduced several AI-driven features:

1. Recall – A memory-like feature in Windows that captures snapshots of user activity, allowing users to retrieve past information effortlessly.
2. Cocreator in Microsoft Paint – An AI-powered tool that follows brush strokes and text-based instructions to generate AI-assisted images, even allowing users to adjust the level of AI creativity.
3. Live Captions – Now supports real-time translation into 44 languages.
4. Enhanced Windows Copilot – Redesigned to leverage on-device AI processing for faster and more efficient assistance.

Developer Tools & AI Integration

As a developer-focused event, Microsoft Build 2024 introduced new tools to empower developers:

1. Windows Copilot Runtime – Described by CEO Satya Nadella as the AI equivalent of Win32 for the graphical interface, this framework provides access to 40+ AI models for developers.
2. Phi-3 AI Models – New small language models, including Phi-3-small, Phi-3-medium, and Phi-3-vision, which can reason over real-world images.
3. Phi-Silica – A compact AI model optimized for on-device AI processing on Copilot+ PCs.
4. Native support for PyTorch & WebNN – Now available in developer preview.
5. Azure AI Studio – Now generally available, with enhanced security, safety features, and support for custom AI models.
6. GitHub Copilot Extensions – Enables third-party integrations within GitHub Copilot.
7. GitHub Copilot for Azure – Allows natural language-based deployments and access to Azure resources.

AI in Action: Live Demo Highlights

One standout demo showcased Copilot in Minecraft, where the AI assistant observed the game screen in real time and provided contextual gameplay advice. This demonstrated how Microsoft’s partnership with OpenAI enables natural voice interactions, a feature introduced at OpenAI’s Spring 2024 event.

Building Custom Copilots

Microsoft introduced tools for custom Copilot development, catering to users of all skill levels:

1. No-code Copilot creation – Users can generate a basic Copilot using documents stored in SharePoint.
2. Copilot Studio – A low-code solution for expanding and customising Copilots.
3. Visual Studio Integration – Experienced developers can build Copilot Extensions for enhanced functionality.

Microsoft Teams Gets an AI Upgrade

A major announcement was Team Copilot, a new AI-powered assistant in Microsoft Teams that can:

1. Facilitate meetings and take notes.
2. Collaborate in group chats.
3. Act as a project manager.

Team Copilot will be available in preview later this year.

Microsoft Build 2024 showcased a new era of AI-powered computing, providing developers with powerful tools to build next-generation AI experiences. With Copilot+ PCs, on-device AI, and an expanding AI ecosystem, Microsoft is setting the stage for the future of intelligent computing.

Computing has taken a major leap forward with the integration of Neural Processing Units (NPUs), revolutionizing AI-driven tasks. Let’s break down how NPUs compare to CPUs and GPUs.

CPUs: The Generalist Brain

The CPU (Central Processing Unit) is the brain of every computer, translating high-level instructions into machine code while managing system interactions. Over time, CPUs have evolved with multiple cores, faster clock speeds, and improved power efficiency to handle increasingly complex tasks.

GPUs: The Parallel Processing Powerhouse

With the rise of 3D graphics and gaming, specialized processors were needed—this led to the development of GPUs (Graphics Processing Units). Unlike CPUs, which typically have 4 to 16 powerful cores, GPUs contain thousands of smaller cores optimized for parallel processing, excelling at tasks like matrix operations and vector calculations. Their efficiency in handling massive workloads made them ideal for AI computations, leading to widespread adoption in machine learning and deep learning.

NPUs: The Future of AI Computing

As AI workloads became more demanding, NPUs (Neural Processing Units) emerged as dedicated AI accelerators. Designed to handle AI-specific computations like matrix multiplication with low latency and high throughput, NPUs are now integrated into modern computing architectures.

1. Google refers to its NPUs as Tensor Processing Units (TPUs).
2. Apple calls its version the Neural Engine.
3. Microsoft recently introduced Copilot+ PCs, capable of executing 45 trillion operations per second.

The Rise of Edge Computing

One of the biggest advantages of NPUs is their role in edge computing. Traditionally, AI models like GPT and Gemini process data in the cloud, which can be costly and pose privacy risks. NPUs enable local devices to run smaller AI models like Phi and Gemma, preserving user privacy while improving efficiency. These models can still offload complex tasks to the cloud when needed, striking a balance between performance and security.

A Win-Win for the Future

With NPUs, AI computing is becoming more efficient, energy-saving, and privacy-focused. As this architecture continues to evolve, it paves the way for new advancements in AI and computing. What’s next? The future looks promising!

Google I/O introduced several new features focused on expanding the Google Gemini ecosystem.

Gemini Advanced and Pro 1.5

Google has now integrated Gemini Pro 1.5 into its advanced platform, making it accessible beyond just developers. This model boasts a massive 1 million-token context window, allowing users to process extensive documents, including Google Docs, PDFs, and Word files from Google Drive. With this capability, users can upload large encyclopedias and retrieve specific information efficiently. The million-token window supports processing:

1. An hour’s worth of video
2. 11 hours of audio
3. 30,000 lines of code
4. 700,000 words in a document

Gemini Flash: Low Latency Multimodal Model

Google also announced Gemini Flash, a low-latency multimodal model with advanced reasoning and a million-token context window. Compared to other Gemini models, it offers superior speed and efficiency. Available now in Google AI Studio and Vertex AI, developers can also apply for an extended 2 million-token context window. This model is Google’s response to OpenAI’s GPT-4o, introduced just a day before I/O. While not as advanced, Gemini Flash offers significantly lower pricing:

1. $0.35 for input up to 128,000 tokens
2. $0.70 for up to 1 million output tokens
3. $0.53 per million output tokens for prompts under 128,000 tokens
4. $1.05 per million output tokens for longer prompts

This is much cheaper than GPT-4o, which currently costs $5 per million input tokens and $15 per million output tokens.

Context Caching and New Vision Features

A major upcoming feature is context caching, enabling users to store large documents for reuse without re-uploading them for every query. Additionally, new vision capabilities have been added to Gemma, Google’s open-source model based on Gemini. Previously, Gemma was available in two versions: RecurrentGemma and CodeGemma. Now, Google has introduced PaliGemma, a multimodal model with vision capabilities.

Gemma, initially available in 2-billion and 7-billion parameter sizes, will soon expand to a 27-billion parameter version for enhanced performance.

AI in Search and Google Products

Google is integrating Gemini into search, providing real-time AI-powered overviews. These AI-generated search results currently support queries related to dining and recipes and will soon extend to movies, hotels, shopping, and more.

Future AI Innovations

Google previewed Project Astra, an AI agent capable of continuously processing and responding to real-time video inputs. While impressive, its release is expected later this year. Google also introduced Imagen 3, the latest AI image-generation model, with improved realism, better prompt adherence, and enhanced text rendering.

For AI-generated videos, Google VEO was announced as a competitor to OpenAI’s Sora, offering long-form generative AI video capabilities. Both Imagen 3 and VEO will be available through Google Labs, where users can sign up for early access.

While many of these features are not yet available, Google’s strategy revolves around integrating AI across its product suite to enhance productivity. 

OpenAI held its Spring Update on 13th May 2024, announcing the release of its latest model, GPT-4o (‘o’ for ‘omni’), along with several other significant updates. Here’s a breakdown of what you need to know.

The primary highlight of the update is GPT-4o, OpenAI’s first model that seamlessly integrates text, image, and audio. This means that operations previously requiring multiple steps—such as transcribing speech, processing the text through GPT, and converting the response back into speech—are now streamlined, reducing latency and enhancing efficiency.

GPT-4o is described as being twice as fast as GPT-4 Turbo. Additionally, the GPT-4o API offers twice the speed, 50% lower costs, and five times higher rate limits compared to GPT-4 Turbo. Essentially, GPT-4o has now replaced GPT-4 Turbo as OpenAI’s benchmark model.

Key Updates You Need to Know

1. ChatGPT with GPT-4o Is Now Free for Everyone

With the introduction of GPT-4o, OpenAI has made its most powerful model accessible to all users, including those without an account. Free users now have access to GPTs from the GPT Store, along with features such as vision, web browsing, memory, and advanced data analysis.

Premium, Team, and Enterprise users benefit from enhanced performance, higher usage limits (80 messages every three hours), and early access to new features. When users exceed their limits, ChatGPT will revert to GPT-3.5 Turbo, as was the case previously.

2. Multimodal AI Is Becoming the Default

The long-standing vision of an omniscient, voice-controlled AI assistant is becoming a reality. Over the next few weeks, OpenAI will introduce full live voice and vision capabilities for ChatGPT. Users will be able to engage in fluid, real-time conversations with the AI, show it objects through their device’s camera, and receive instant feedback.

Significant improvements to the audio model include:

1. Reduced lag for more natural conversations
2. The ability to interrupt the AI mid-sentence or during reasoning
3. AI responses that adapt to the user’s emotional tone

Additionally, the improved live vision model enables ChatGPT to analyse what the camera sees in real time. During the launch demo, the AI guided users through solving a maths problem, providing real-time feedback as they wrote it out on paper. This suggests an imminent future where AI assistants actively collaborate on tasks rather than merely responding to queries.

3. ChatGPT Desktop App for macOS

To facilitate the use of these multimodal features, OpenAI has introduced a ChatGPT desktop app with enhanced integration capabilities. Currently available only for macOS, the app includes voice mode and image upload functionalities. Future updates will incorporate GPT-4o’s advanced voice and video capabilities.

The desktop app allows users to:

1. Interact with ChatGPT directly without opening a browser
2. Share screenshots for AI-assisted problem-solving
3. Receive AI-generated feedback on images, graphs, and coding issues

4. New Security Considerations

The broader availability and enhanced multimodal capabilities of ChatGPT introduce new security challenges, particularly for enterprise users. As ChatGPT evolves into a more intuitive collaboration partner—capable of analysing screens and responding via voice—businesses must establish clear policies and safeguards to ensure responsible AI usage.

There is a significant difference between manually copying and pasting information into an AI tool versus effortlessly sharing sensitive data via an integrated desktop app. With these new capabilities, robust oversight and security measures are more critical than ever in privacy-sensitive environments.

The Future of AI Is Here

Since the launch of ChatGPT in November 2022, OpenAI has remained at the forefront of the generative AI revolution. The introduction of GPT-4o, alongside the expansion of ChatGPT’s accessibility, reinforces the company’s commitment to leading the AI industry towards a future dominated by multimodal, real-time conversational AI.

As other AI companies follow suit, it is clear that multimodal AI is set to become the new standard for human-AI interactions.

Imagine being able to describe a video and have it appear as if by magic. Well, OpenAI’s Sora allows you to do just that.

Sora is a text-to-video diffusion model. It receives a prompt, begins with a noisy sequence, and then iteratively removes the noise until a clean and crisp video emerges. It heavily relies on transformer architecture—the same model structure that has powered groundbreaking technologies such as ChatGPT and DALL·E.

While this innovation is incredibly exciting, it also raises concerns, particularly regarding the potential for generating misinformation.

At present, you may not yet have access to Sora or any video generation tool, but there are ways to prepare for this technology.

Firstly, you can refine your prompt engineering skills using a text-to-image generation tool, as these abilities are likely to be transferable to Sora. Secondly, familiarising yourself with cinematographic terminology can be beneficial. Learn the names of different camera angles, and research the cameras and lenses used to film your favourite movies.

Responsible AI is crucial for any organisation that wants to use AI—not only to ensure legal compliance, as numerous AI laws are on the horizon, but also to maintain customer trust. Responsible AI involves the development and deployment of ethical, trustworthy, and lawful algorithms, maximising the benefits of artificial intelligence while minimising legal, reputational, and financial risks.

To achieve responsible AI, effective AI governance is essential. Responsible AI governance ensures that AI systems prioritise human interests, fairness, transparency, explainability, privacy, safety, security, and accountability. It is not solely the responsibility of data scientists and software engineers but requires a cross-functional effort involving multiple stakeholders. Moreover, it should not be treated as an afterthought but integrated into AI development from the outset. Effective governance must consider technology, people, policies, and processes to ensure AI systems are responsible by design.

AI governance is a complex and evolving field. As AI ethics expert Rumman Chowdhury noted, “It’s the $10 billion question—who should govern AI, and at what level?” Some governance challenges must be addressed on a global scale, while others require regional or national legislation and enforcement. AI standards, bias audits, and other elements will contribute to a broader responsible AI assurance ecosystem. Every organisation developing AI must also establish internal governance structures.

Guiding Principles for AI Governance

1. Integrate Responsible AI from the Start
   Responsible AI should not be an afterthought. For example, privacy by design—such as using synthetic data—ensures compliance and efficiency. Similarly, fairness, explainability, and accountability should be embedded from the ideation phase rather than being retrofitted later.
2. Adopt a Multidisciplinary Approach
   AI governance is not solely the domain of data scientists. It requires input from diverse teams, including legal experts, social scientists, and ethicists. A broad range of perspectives helps organisations build AI systems that are fair, transparent, and accountable.
3. Continuously Monitor AI Systems
   AI governance is not a one-off process. It requires ongoing monitoring throughout the AI lifecycle—from ideation and development to deployment and post-deployment evaluation. Implementing robust processes ensures AI remains responsible over time.
4. Invest in AI Skills and Training
   There is a global shortage of AI talent, particularly in responsible AI. Organisations must invest in upskilling their workforce to develop in-house AI governance capabilities and ensure compliance with evolving regulations.

AI Regulations Around the World

AI regulation is a crucial component of AI governance. While the field is still developing, different regions are taking varied approaches:

• European Union: The AI Act is the first comprehensive AI law, setting a precedent for global regulations.
• Singapore: The government has introduced an AI governance framework rather than strict legislation.
• United States: There are ongoing discussions about AI regulation, but no federal law exists. However, the White House has issued guidance on responsible AI principles to influence policy development.

Since AI regulation is still evolving, organisations should proactively adopt responsible AI principles—such as fairness, privacy, and explainability—to ensure compliance with future laws.

Business Benefits of Responsible AI Governance

Building responsible, human-centred AI is not just the right thing to do; it also offers several business advantages:

1. Public Trust and Business Sustainability
   Responsible AI helps organisations maintain public trust and social licence to operate.
2. Financial Performance
   A study by Accenture found that companies experiencing the highest revenue growth from AI were also those with robust responsible AI governance in place.
3. Operational Efficiency
   Implementing responsible AI principles early on reduces costly errors. Many organisations have spent years and millions developing AI models, only to withdraw them due to bias or compliance issues. Embedding fairness, privacy, and explainability from the start prevents such setbacks.
4. Talent Attraction and Retention
   AI talent is scarce, and organisations with strong responsible AI governance are more attractive to top talent. AI professionals prefer to work for companies committed to ethical AI practices.

By prioritising responsible AI governance, organisations can build trust, ensure compliance, improve financial performance, and attract top AI talent. As AI regulations continue to evolve, a proactive approach to governance will help organisations stay ahead of the curve.

General AI is the holy grail of artificial intelligence research. Instead of building products or tools to solve specific problems—such as chatbots, AI-generated art, or poetry—what if we created a tool capable of doing all of these? A system that could emulate human curiosity, gather new information, and solve a wide range of problems? However, general AI is still a long way off and remains an aspirational goal.

General AI has the potential to revolutionise everything, even more so than generative AI. When it arrives, it will redefine what work means. True general artificial intelligence will transform organisations, institutions, and possibly even human relationships. One of its first major impacts will be in challenging our traditional concepts of work. If general AI enables automation of tasks—starting with basic mechanical or factory jobs and quickly progressing to finance, HR, and strategic design—the question will no longer be what general AI can do instead of a human, but rather what humans can do to create new value by partnering with AI to achieve better business outcomes.

General AI as a Workplace Colleague

As an enterprise leader, it helps to think of today’s AI as a talented junior colleague—someone at the start of their career, capable of solving specific, well-defined problems and presenting a few options for next steps. In contrast, general AI represents a senior colleague—someone who can be given a problem set, innovate potential solutions, test them, determine what works best, and return with a fully developed recommendation that can be deployed at scale across an organisation. This is the difference between performing a single task and being responsible for an entire business function. 

With general AI, humans will be able to work alongside AI to build more efficient, creative, and dynamic organisations.

Misconceptions About General AI

A common misconception is that general AI will displace humans from the workplace. However, everything we currently know suggests that this is unlikely. Even if general AI enhances our ability to strategise and transform business operations, it will function best as a collaborative tool. Humans will still be required to guide AI, define the problems that need solving, and provide essential context. While AI brings computational power and automation, humans contribute uniquely human qualities—curiosity, creativity, empathy, and contextual understanding. 

When working together, humans and AI have the potential to develop solutions that outperform either working alone.

The Future of General AI

Modern AI has already given us glimpses of what is possible, but achieving true general AI will require us to build curiosity into our technological systems. Current estimates from scientists and researchers suggest that the first major breakthroughs in general AI could occur within the next 5 to 15 years. 

We are only beginning to understand how these tools can enhance human experience—whether by transforming creative expression through AI-generated poetry and art, or by improving workplace collaboration and efficiency.

General AI will mark a step change in our relationship with technology. It will eliminate tedious tasks, automate mundane processes, and free us from repetitive work such as scheduling or administrative duties. This will allow humans to focus on what they do best—building connections, identifying new opportunities, and generating creative ideas that inspire and improve the world around us.

What Are Large Language Models (LLMs)?

At the forefront of the artificial intelligence revolution is a technology you may have heard of—large language models (LLMs). An LLM is a type of neural network, a computing system designed to mimic the structure of the human brain. Its primary function is to process information and generate meaningful results.

How Do LLMs Work?

LLMs are trained on vast amounts of human language data to understand linguistic patterns. They can generate text that mirrors these patterns and answer questions in a human-like manner. While an LLM is not capable of original thought, it can produce content that appears creative by drawing on the patterns it has learned. This process is what we refer to as generative AI.

To achieve this, LLMs undergo multiple training phases where information is tokenised—converted into numerical representations that can be processed more efficiently. These models often handle billions or even trillions of parameters. However, they do not perceive words as humans do.

Tokenisation in LLMs

A token may not represent an entire word; for example, “running” might be split into “run” and “ing.” Frequently paired words, such as “king” and “queen,” might be treated as a single token. The ultimate goal is to predict the next token in a sequence, enabling the LLM to generate coherent text when responding to a prompt.

The Role of Transformers and Attention Mechanisms

At the core of LLMs is a mechanism called a transformer. Unlike traditional models, an LLM does not give equal attention to every word in a prompt. Instead, it analyses tokens to determine which ones carry the most significance in the given context—similar to how humans focus on key points in a conversation while ignoring less relevant details.

This attention mechanism, a fundamental feature of transformers, was introduced in Google’s research paper “Attention Is All You Need.”

Are LLMs Really Answering Questions?

Interestingly, LLMs do not truly answer the questions posed in prompts. Instead, they attempt to continue the text based on the patterns they have learned. They are, in a sense, “tricked” into answering questions because they predict what words would logically follow in a given context—treating prompts as part of a larger, incomplete document.

They continuously evaluate the entire sequence, including the text they have already generated, making predictions word by word.

The Future of LLMs

There are numerous LLMs available today, developed by companies such as OpenAI, Google, and Meta. While each model may function slightly differently, they all share a common purpose: to assist humans by answering questions, facilitating brainstorming, and enhancing efficiency.

Understanding Modalities in AI

Most prompt engineering today focuses on a single input and single output approach. When we refer to modal, we mean a mode of communication, such as:

1. Text
2. Images
3. Videos
4. Voice
5. (Potential future modalities)

However, AI is evolving beyond single-modal inputs, leading to the rise of multimodal models that can process multiple types of inputs simultaneously.

The Rise of Multimodal Models

By the end of 2023, several AI models introduced multimodal capabilities, enabling users to combine different input types in prompts. One notable example is GPT-4 Vision, which allows users to:

1. Upload an image
2. Provide an instruction or prompt
3. Receive a response based on the image content

Example Use Case

Imagine you upload a chart from a slide deck and ask:
“What is in the chart?”
The model can analyse the image and describe:

1. The trends
2. The numbers
3. Other insights

How Is This Different from OCR?

Previously, Optical Character Recognition (OCR) was necessary to extract text from an image before analyzing it. Now, multimodal models allow users to ask questions directly about images, eliminating extra steps and enhancing AI capabilities.

Popular Multimodal Models

Some of the most advanced multimodal AI models currently available include:

1. GPT-4 Vision (by OpenAI)
2. Gemini Models (by Google)
3. LLaVA (Open-source model)

As AI research progresses, more powerful multimodal models are expected to emerge in the coming years.

Security Risks in Multimodal AI

With increased capabilities come new security risks, as multimodal models can process inputs that are difficult for humans to detect. Potential threats include:

1. Unsafe or censored images being used as prompts
2. Hidden messages embedded in images, which are visible to AI but not to humans
   1. Similar to colourblind tests, where specific patterns are only visible under certain conditions
   2. Comparable to hidden white-text keywords on resumes that can be detected by AI but not by human reviewers

Ensuring Safe Multimodal AI Applications

When designing multimodal AI applications, developers must consider new attack vectors and implement robust security measures to prevent misuse. Multimodal AI is a game-changer, unlocking new possibilities while also introducing new challenges. As the field evolves, balancing innovation with safety will be crucial.

For AI to be truly useful in a development setting, it must be configurable, with features like state management and dynamic controls. Initially, OpenAI’s API required developers to manually structure system, assistant, and user messages while managing state by storing message-response pairs in a database. This made building custom AI agents complex, expensive, and time-consuming.

The Introduction of the Assistant API

To streamline AI development, OpenAI introduced the Assistant API, a developer-friendly version of ChatGPT’s Custom GPTs. This new API allows the creation of custom AI assistants with unique instructions, tools, and knowledge retrieval capabilities. Additionally, it enables stateful conversations without requiring developers to store chat history themselves. Assistants can be managed both programmatically via the API and through the OpenAI Playground.

Key Components of Assistant GPTs

The Assistant API revolves around three core elements:

1. Assistants – Custom AI models with unique IDs, system instructions, tool configurations, and knowledge retrieval capabilities. They can use function calls, a code interpreter, and reference uploaded documents.
2. Threads – Each thread represents a separate conversation, maintaining chat history and enabling stateful interactions. Developers can have multiple parallel threads.
3. Runs – A run consists of a prompt-response pair, allowing an assistant to perform multiple steps within a single interaction, such as executing a function call or using the code interpreter.

State Management and Token Consumption

Assistants maintain context by passing the entire conversation history with each new message. While this ensures continuity, it also increases token consumption as conversations grow. Developers need to optimize interactions to balance efficiency and cost.

The Impact of Assistant GPTs

With this new API, developers can now rapidly deploy custom AI solutions, integrate them into various applications, and create assistants tailored to specific business needs. The Assistant API simplifies AI development, making powerful, adaptive AI experiences more accessible and scalable.

Google Gemini is a generative AI model designed for multimodal capabilities, meaning it can process text, images, and videos simultaneously. Built for diverse computational needs, Gemini integrates with Google Cloud services like Vertex AI, making it a powerful tool for AI-driven applications.

Versions of Gemini

Gemini comes in three versions, each catering to different levels of complexity:

1. Nano is the smallest model, designed for on-device usage, particularly in mobile applications.
2. Pro is available in public preview and is ideal for intermediate-level projects.
3. Ultra is the most advanced model, currently in private preview, meant for large-scale AI operations.

Both Pro and Ultra have two variants: a Standard version optimized for text-only processing and a Vision version designed for multimodal tasks, handling text, images, and videos.

Key Features and Capabilities

One of Gemini’s standout features is its ability to process multiple types of data simultaneously. This allows developers to generate code from visual inputs, perform multimodal Q&A, and interact with AI in innovative ways.

Its advanced coding abilities include automated code optimization, predictive coding suggestions, and UI-to-code conversion. Additionally, function calling and retrieval-augmented generation (RAG) enhance complex queries over multimodal data, making interactions with AI more intuitive.

Responsible AI and Safety Measures

Google has implemented strong safety measures to ensure responsible AI development. These include safety ratings and filter thresholds for content moderation, fairness and bias-checking algorithms, and protections against harmful content such as harassment, hate speech, and explicit material.

Learning Resources

Developers and learners can explore Gemini through official documentation, GitHub repositories, interactive tutorials, and YouTube playlists. 

Google Gemini represents a significant advancement in AI development, offering versatile multimodal interactions and advanced coding solutions, making it a valuable tool across various industries.

Claude is a large language model (LLM) developed by Anthropic, designed to rival OpenAI’s models in terms of capability. When selecting an LLM, it’s important to choose based on the specific task you want to accomplish.

How to Choose the Best LLM for Your Task

To determine which model is best suited for your needs, the most effective approach is to create a custom testing dataset. By comparing how different models perform on your dataset, you can identify which one meets your requirements.

Reasons to Choose Claude

There are several reasons why developers might choose Claude over other LLMs:

1. Task-Specific Performance: The first consideration is always the task you’re trying to perform. Different models may excel in different areas, so testing is crucial.
2. Accessibility and Privacy: Claude is offered by Anthropic and can be accessed via Google Cloud Platform or Amazon Web Services (AWS). If you don’t have access to either of these platforms, you may not be able to use Claude.
3. Cost and Prompting Style: Costs may vary across different platforms, and certain users might prefer one model’s prompting style over another. Each LLM processes prompts slightly differently, so it’s essential to find the one that best suits your own prompting style.

Claude’s Long Context Length

In mid-2023, one of the standout features of Claude was its exceptionally long context length, reaching up to 100,000 tokens. However, by late 2023, many advanced LLMs now support context lengths ranging from 32,000 to 200,000 tokens. This feature, once a distinguishing characteristic, is now more common, and future progress in LLM development will likely focus on communication, prompting style, and output preferences.

How to Access Claude

Claude is available through three main channels:

1. Anthropic’s Official Site: You can access the Claude chatbot and API directly from Anthropic’s website.
2. AWS Bedrock: Claude is also accessible via AWS’s platform, enabling easy integration into various applications.
3. Google Cloud Platform: Claude can also be accessed through Google Cloud, which allows developers to build various LLM-based applications, including coding, copywriting, and more.

Claude, like other large language models, offers powerful capabilities for a variety of applications. However, when choosing between models, developers must consider factors like task requirements, accessibility, cost, and prompting preferences. Switching between LLMs may require fine-tuning prompts, as each model interprets instructions differently.

Just like how gaming controllers evolved from the simple joystick of the Atari 2600 to more advanced models, the technology behind AI language models has undergone significant upgrades. One of the latest advancements is GPT-4, the fastest-growing technology app of all time. This update promises to take AI-driven interactions to a whole new level.

What is GPT?

GPT stands for Generative Pre-trained Transformer, a neural network that creates new content such as stories, art, and much more. It’s “pre-trained” using vast amounts of data and is designed to understand key points from human input and infer the user’s intent. For example, if you ask, “How do I build the best house in Minecraft?” GPT identifies the keywords and tries to provide an answer based on assumptions that are most likely to be correct for a variety of scenarios in the game.

GPT-3 vs. GPT-4: What’s New?

GPT-4 is an upgrade to its predecessor, GPT-3, bringing several improvements:

• Better Reasoning: GPT-4 can provide more concise and logical answers, but it may take longer to respond.
• Improved Accuracy: It reduces hallucinations (nonsensical or untruthful answers), making the model more reliable.
• Increased Computational Power: While more powerful, GPT-4 requires greater computational resources, which can lead to higher costs.
• Passes the Bar Exam: GPT-3 passed the bar exam in the lower 10th percentile, while GPT-4 achieved the top 10th percentile.

Enhanced Capabilities in GPT-4

1. Larger Input Capacity: GPT-4 can process up to 25,000 words of text, which is a major upgrade over GPT-3. This allows it to handle longer texts, such as conference transcripts, and summarise them with key topics and time codes.
2. Steerability: You can now control the model’s personality, verbosity, and style. For example, GPT-4 can be set to a Socratic mode, where it answers questions with more leading questions, encouraging users to uncover answers on their own.
3. Image Understanding: GPT-4 can understand photos and graphics, such as interpreting the contents of a fridge or reading data from charts and graphs. While this feature is not yet publicly available, it is expected to roll out soon.

Real-World Applications of GPT-4

GPT-4 has already made its way into various industries:

• GitHub Copilot: Helping developers become 88% more productive.
• Duolingo, Stripe, and Morgan Stanley: Integrating GPT-4 into their products to enhance user experience and capabilities.
• Microsoft’s Semantic Kernel: An open-source SDK that makes it easier for developers to integrate GPT-4 into applications, including Microsoft 365 apps like Word, Excel, and PowerPoint.

The Future of AI with GPT-4

GPT-4 is like the ultimate upgrade to an already powerful AI controller. It’s going to change how we interact with AI, from enhanced reasoning to image interpretation, and bring more creative possibilities to the table. With the rapid adoption of GPT-4 across industries, we can expect to see more productivity gains and innovative features rolled out in the near future. Get ready for a whole new level of AI interaction coming soon to your favourite apps!

ChatGPT, an online application launched in November 2022, gained 1 million users in just five days. It allows users to engage in conversations with a set of technologies known as GPT.

GPT stands for Generative Pre-trained Transformer. Its goal is to generate or create new content by processing data through a model that’s pre-trained with up to 175 billion parameters. To manage this vast amount of data, it uses a transformer model, which is highly skilled at understanding how people construct sentences. The technology was developed by OpenAI, a company whose mission is to create Artificial General Intelligences (AGI) that can understand any intellectual task a human can do. The aim is to build AIs that benefit humanity, not replace it.

OpenAI’s Other Products

In addition to ChatGPT, OpenAI has developed other innovative products, including DALL-E 2, an AI for generating realistic art, and Whisper, a speech recognition, translation, and transcription system that mimics human-like synthetic speech.

How AI Can Perform Creative Tasks

You may be wondering how AI is capable of performing creative tasks that were once exclusively human. Developers have designed models and algorithms that attempt to replicate how humans solve creative problems. For instance, during my time working at a newspaper, I watched artists create portraits. They would gather reference material, including photos from various angles and perhaps some inspirational styles. They would then translate that material into an original portrait. While this process seemed magical to many, developers recognise it as a series of repeatable steps that lead to predictable results. In essence, it’s an algorithm.

ChatGPT: A Conversation-Focused Tool

ChatGPT, as the name suggests, is primarily focused on conversations. It’s capable of writing essays, scripts, resumes, songs, and even performing tasks like writing and debugging code. GPT models, including ChatGPT, are autoregressive, meaning they predict results based on past inputs. This is why ChatGPT often appears human-like, as it predicts not just what you asked for, but what you likely mean.

Limitations of ChatGPT

Despite its capabilities, ChatGPT has some limitations. It can occasionally provide wrong answers and do so with confidence. Its training was based mostly on data up to 2021, so it lacks knowledge of more recent events. ChatGPT was trained using human feedback to fine-tune the model, making it an improvement over its predecessor, and is currently regarded as version 3.5 of GPT. While it can generate content, it cannot evaluate whether that content is subjectively good or bad.

The Future of ChatGPT

Looking ahead, I believe ChatGPT will accelerate the development of tasks by helping humans iterate on ideas more quickly. ChatGPT, like other AI technologies, is more than just an application. APIs are available that allow developers to create their own products based on these models. ChatGPT itself is simply an application built on the GPT API.

Opportunities in the AI Space

The biggest opportunities in the AI space lie in the hands of developers who understand how to use APIs, entrepreneurs who can leverage these technologies to create exciting new products, and professionals who know how to work with AIs and maximise their potential. As one person put it, you may not be replaced by ChatGPT, but you might be replaced by someone who knows how to use it.

In the world of generative AI, a prompt is essentially the way you communicate with the AI. It can be as simple as a question or as complex as a detailed construct that incorporates various components.

What is a Prompt?

A prompt can contain a variety of elements such as instructions, a question, input data, or examples. It serves as the foundation for how you interact with the AI. A simple prompt might ask a question, while a more complex one could involve multiple examples, instructions, or even code, depending on the task at hand.

What is Prompt Engineering?

Prompt engineering is an emerging discipline that is difficult to define at this point because it’s being shaped as we speak. It includes all the necessary components for managing prompts at scale. Given that prompts can range from simple questions to intricate sets of instructions, examples, and code, effectively managing and engineering these prompts is crucial when building AI-powered products.

Managing Prompts at Scale

When building products with AI at their core, managing prompts at scale becomes essential. This involves ensuring that the AI can interpret and respond to prompts effectively. It’s not just about creating prompts, but also about testing and maintaining their quality. One key aspect of this process is Quality Assurance (QA). With large-scale systems, you might have multiple variations of a prompt, and you need to assess their effectiveness, quality, and performance. Feedback loops, sometimes involving human input, are used to fine-tune prompts based on real-world usage.

The Role of Feedback in Prompt Engineering

QA in prompt engineering goes beyond just testing for correctness. It includes evaluating the nuances of different components of a prompt and ensuring that the AI responds in the desired way. This feedback loop is critical in refining prompts and ensuring they produce high-quality outputs.

The Future of Prompt Engineering 

Prompt engineering is a key component of generative AI, and it’s evolving rapidly. It’s even starting to leverage itself in some ways. For example, you can prompt a language model to generate a prompt, which then feeds into a text-to-image model to create an image. This kind of self-referential AI prompting is a fascinating development, suggesting that AIs could communicate with each other in the near future as part of the prompt engineering process.

Challenges of Scaling and Managing Prompts

As generative AI grows, managing these processes at scale will present various challenges. For instance, how do we prevent users from inputting prompts that cause the AI to generate inappropriate or off-track responses? How do we ensure that prompts are effective and maintain quality over time? Productionising AI systems and keeping them working at scale will require careful engineering and robust systems in place.

A New Discipline in AI Management

The rapid growth of generative AI is creating a new discipline that revolves around managing the way we interact with AIs. As this field evolves, the discipline of prompt engineering will become more formalised, with processes and systems for creating, testing, and maintaining effective prompts becoming more sophisticated.

Generative AI is poised to revolutionise not only our industry but potentially the world at large. As the technology matures, we are likely to see its applications expand in unexpected and transformative ways. Prompt engineering will play a critical role in making sure that these advancements lead to high-quality, useful, and reliable AI applications.

Generative AI: Creating New Patterns

Your brain stores a vast amount of information about familiar objects and, in doing so, creates rules to predict what comes next. Recently, we’ve learned that we can not only teach computers to recognise patterns but also ask them to create new ones. This is known as generative artificial intelligence.

How Generative AI Works

Computers are excellent at detecting patterns in a useful way. For example, phone cameras are great at recognising faces and can even be trained to identify if that face belongs to you. They do this by looking for patterns in the image, mapping out distances between different parts of the face (a process known as biometrics). With this information, the computer can predict whether the pattern matches what it already knows about you.

The breakthrough in generative AI comes when you realise that you can teach the computer not only to recognise patterns but also to generate new patterns based on what it has learned. For instance, if you trained a computer using a series of nose images, you could ask it to analyse the pixels in the image and learn what a nose looks like. With enough data, the computer could then generate a series of new pixels that resemble a nose. This concept can be extended to faces and, eventually, entire images.

Applications of Generative AI

You can see this technology in action on websites like this-person-does-not-exist.com, where generative AI creates random human faces. In fact, the site claims that 90% of these faces go unrecognised by an average person, and 50% are not even identified by professional photographers.

This same approach can be used for deep fakes, where a computer is trained to replace an existing face with another, based on a set of images you provide. But generative AI isn’t limited to just images – it can also be applied to music, where it learns from various genres to create original compositions.

For example, tools like Compose AI use GPT-3, a technology trained on billions of parameters, to write human-like text. This has been utilised by companies like GitHub in a tool called Copilot, which assists programmers by writing entire functions, significantly reducing the time it takes to generate code.

Limitations of Generative AI

While generative AI is certainly impressive, it does have its limitations:

1. Massive Data Requirement: It needs vast amounts of data to effectively generate new information.
2. Unpredictable Results: It doesn’t always generate desirable outcomes. For instance, with tools like Copilot, you cannot fully trust the code it generates to work properly.
3. No True Innovation: Generative AI can’t create entirely new things. It only combines information from patterns it has already learned.

Generative AI is a disruptive technology that can process large datasets, generate numerous options, and streamline repetitive tasks. While it may not yet be able to truly innovate, it serves as an invaluable tool for assisting humans in their work, particularly when it comes to data processing and task automation.

A Growing Technology with Serious Implications

Facial recognition in action – computer algorithms, powered by technologies like high-speed internet, cloud services, high-resolution cameras, machine learning, and AI, that can identify people based on photos or videos.

How Does Facial Recognition Work?

It all begins with data collection. Facial recognition companies have been gathering available photos and videos online for years, using this data to train algorithms. These algorithms find faces, identify recognisable features, and build models that enable recognition from photos and videos. Whenever you or someone else uploads a photo of you to cloud storage, social media, or any public platform, it’s likely that it will be used to train these systems to improve their ability to recognise faces.

Facial recognition technology is becoming ubiquitous. You can unlock your phone or computer just by looking at them. Smart doorbells use facial recognition to alert you when someone visits. Malls, airports, and other public places use it to track customers, offer tailored services, and reduce crime. Government agencies use it to speed up identity verification, and schools monitor students with it. Law enforcement uses it for crime prevention and investigation. In fact, whether in public or private spaces, if there’s a camera, facial recognition is probably at work behind the scenes.

The Pros and Cons of Facial Recognition

On the positive side, facial recognition can make life simpler – from unlocking your phone to verifying your identity when you travel or attend an event. It can also be an important tool in crime prevention and investigation.

However, there are significant issues to consider. First, facial recognition is powered by machine learning and AI, and the tools and models behind these technologies are only as good as the data they are trained on. Studies and real-world examples have uncovered biases in these systems, such as racial bias. This has led to serious negative outcomes, particularly for underrepresented groups, like Black Americans being wrongfully charged with crimes due to misidentification by commercial facial recognition systems used by law enforcement.

Secondly, facial recognition raises serious privacy concerns. The data used to train these algorithms often comes from photos and videos gathered from the internet or surveillance cameras without the consent or even knowledge of the people involved. This means your face could already be part of a facial recognition system, even if you never agreed to it. And once your face is in the system, getting it removed is almost impossible. With the increasing prevalence of facial recognition, we are moving toward a future where someone or something will always be able to track our every move.

The Future of Facial Recognition

As this technology advances, systems will improve and will be able to distinguish between someone actually being in an image and simply having their image included. Efforts are underway to reduce biases and prevent misidentification. But as I mentioned, this technological progress comes at the cost of privacy, which may not be a major concern in open, democratic countries, but could be critical for political dissidents in regions facing social conflict, political oppression, or even war.

Considerations for the Future

If you’re a consumer, educate yourself about where facial recognition is used and how to opt out if you don’t want to be tracked. Most online services, including cloud and social media platforms, offer options to opt out of facial recognition.

If you’re a developer working with facial recognition, make privacy by design and informed consent are core principles of your work. Learn about AI and machine learning bias, and stay up to date with legislation like GDPR (General Data Protection Regulation) and the California Privacy Rights Act (CPRA).

For companies, organisations, or government agencies looking to implement facial recognition, ensure your use is based on informed consent. Apply due diligence when selecting a vendor, and only implement it when absolutely necessary. Also, consider privacy laws when deploying facial recognition technologies.

While facial recognition offers convenience, like unlocking your phone or being notified when your photo is posted online, it’s worth asking: Is the cost to our privacy and security a price we’re willing to pay?

Microsoft Security Copilot

Imagine stepping into the role of a cyber analyst on your very first day, only to be met with a deluge of security alerts. To add to the pressure, your manager requests reports on top threats, leaving you overwhelmed and unsure of where to begin.

Now, imagine if you could simply type a plain-text query, and an intelligent tool would instantly provide step-by-step guidance to help you respond. This is exactly the experience that Microsoft Security Copilot delivers.

Powered by OpenAI’s GPT-4 and running on Microsoft’s Azure infrastructure, Security Copilot offers real-time assistance to cyber analysts, helping them navigate security incidents efficiently and effectively.

How Microsoft Security Copilot Works

At first glance, Security Copilot might look like a simple chatbot. However, behind the scenes, it leverages the vast power of Microsoft Threat Intelligence, processing data from an astonishing 65 trillion daily security signals.

🔹 Understands plain-text queries and provides actionable security guidance
🔹 Analyses real-time security threats and recommends best practices
🔹 Summarises incidents and generates reports for easy sharing
🔹 Identifies vulnerabilities and signs of breaches at an asset level

By automating time-consuming cybersecurity tasks, Security Copilot allows security professionals to focus on higher-value activities, such as strategic threat mitigation and incident response.

Key Features of Microsoft Security Copilot

1. Real-Time Threat Analysis

Security Copilot continuously monitors security signals and instantly identifies ongoing attacks, enabling analysts to take proactive measures before threats escalate.

2. Step-by-Step Incident Response

Instead of sifting through multiple tools, analysts can type a simple query, and Security Copilot will break down the response process into clear, actionable steps.

3. Automated Reporting & Summarisation

Security incidents often require detailed documentation. Security Copilot simplifies this by summarising events, incidents, and threats into comprehensive, shareable reports.

4. Enhanced Threat Intelligence

With access to Microsoft’s global security data, Security Copilot provides insights based on real-world attacks, helping organisations strengthen their cyber defences.

Does Security Copilot Replace Cybersecurity Professionals?

No. Microsoft Security Copilot is not a replacement for security analysts—it is designed to augment their expertise.

By automating routine tasks, Security Copilot enables cybersecurity teams to work faster and more efficiently, ensuring they can focus on critical decision-making and advanced threat mitigation.

Empowering Organisations Across Industries

From software development companies to educational institutions protecting student data, Security Copilot helps teams across industries simplify complex security tasks and achieve more with fewer resources.

Microsoft Security Copilot is not just another security tool—it is a game-changer in the world of cybersecurity.

For Azure developers, what’s your preferred method for building and deploying AI and machine learning models? If your answer isn’t Azure AI Studio, this AI trend is for you.

Microsoft is on a mission to integrate AI into everyday tasks, enhancing productivity across industries. A key part of this initiative is Copilot, an AI-powered assistant designed to help users write, analyse data, generate reports, and even code. To enable businesses to build their own AI-driven Copilots, Microsoft offers Azure AI Studio, a powerful dashboard that simplifies AI model development and deployment.

What is Azure AI Studio?

Azure AI Studio serves as a centralised low-code platform for machine learning development, making it easier to:
✔ Drag and drop modules to create AI workflows without writing extensive code
✔ Utilise pre-trained AI models, including OpenAI’s GPT-4
✔ Seamlessly integrate with Azure Cognitive Services for enhanced functionality
✔ Manage deployment challenges like scalability, security, and governance

For developers, this eliminates the tedious aspects of AI model development—such as data preprocessing, algorithm selection, and iterative training—allowing them to focus on refining their models efficiently.

Key Features of Azure AI Studio

1. Data Ingestion & Grounding

Developers can easily connect both structured and unstructured data to AI models with just a few clicks. The grounding process ensures that AI models have access to accurate, relevant data, improving their responses and decision-making capabilities.

2. Model Catalogues

A model catalogue is a curated repository of pre-trained AI models designed for various tasks, including:
✅ Natural Language Processing
✅ Image Recognition
✅ Recommendation Systems

With Azure AI Studio, developers can easily access and deploy pre-trained models from Azure OpenAI Service and other industry-standard sources.

3. Prompt Engineering with Microsoft Prompt Flow

Getting the right output from an AI model depends on how you prompt it. Prompt engineering involves crafting effective instructions to ensure relevant, high-quality responses.

Microsoft Prompt Flow, integrated within AI Studio, simplifies this process by allowing developers to:
✔ Test and refine different prompts
✔ Connect AI models with data sources
✔ Compare prompt variations for optimal performance

Prompt Flow is compatible with open-source tools like LangChain and Semantic Kernel, making it a versatile tool for AI development.

4. AI Content Safety

Ensuring a safe user experience is critical when deploying AI applications. Azure AI Content Safety provides protection by detecting and filtering:
❌ Hate speech
❌ Violent content
❌ Explicit material
❌ Self-harm indicators

This service, integrated into Azure AI Studio, offers the same safety standards used in Bing Chat and other Microsoft products, allowing developers to maintain ethical AI deployments.

Why Developers Should Explore Azure AI Studio

Azure AI Studio streamlines AI application development, making it accessible to developers of all skill levels. It provides a centralised environment for:
🔹 Loading and managing data
🔹 Training machine learning models
🔹 Deploying AI applications seamlessly

With its low-code approach, Azure AI Studio eliminates complexity, making AI development faster, safer, and more efficient. If you’re a Microsoft developer, it’s time to explore the future of AI development with Azure AI Studio.

For AI systems to be truly useful in a development environment, they must be fully configurable and provide essential features such as state management and dynamic controls. However, when OpenAI first launched its API, developers had to handle all of this manually.

To build custom AI agents, developers needed to:
✅ Declare system, assistant, and user messages with every request
✅ Store message-response pairs in a database to maintain conversation history
✅ Pass stored data back to the API with every message

This was labour-intensive, expensive, and inefficient.

Introducing the Assistants API

In response to these challenges, OpenAI introduced the Assistants API—a developer-friendly version of ChatGPT’s Custom GPTs.

With the Assistants API, developers can now:
✔ Create AI assistants with custom instructions, tools, and knowledge retrieval
✔ Maintain stateful chats without manually storing and managing messages
✔ Develop and configure assistants programmatically via the API or OpenAI Playground
✔ Modify assistants anytime to adapt to new requirements

This significantly simplifies the development of custom AI agents, enabling faster and more advanced AI-powered applications.

How Assistants API Works: The Three Core Components

The Assistants API integrates three key components to deliver a seamless AI experience:

1. Assistants

Each assistant has a unique ID and can be configured with:
🔹 System instructions (defining how it responds)
🔹 Custom tools, such as function calling, code interpreter, and content retrieval
🔹 Knowledge uploads, allowing assistants to reference documents

Developers can create multiple assistants, each tailored to specific tasks or applications.

2. Threads

Each thread represents a separate, continuous conversation with an assistant.
🔹 Threads have unique IDs and maintain chat history
🔹 Developers can create multiple threads for parallel conversations
🔹 Users can switch between threads without losing the state of each conversation

3. Runs

A run refers to a prompt-response pair within a thread.
🔹 Runs can involve multiple steps, such as executing a code interpreter and calling a custom function
🔹 A single prompt can trigger multiple AI processes simultaneously

One key aspect of the Assistants API is that each new message and response is added to the thread. As conversations grow, so does the token count, meaning longer interactions can consume more resources.

The Bottom Line

With the Assistants API, developers can now:
✔ Programmatically create AI assistants in the GPT ecosystem
✔ Customise assistants to perform specific tasks
✔ Seamlessly integrate AI into their applications

This new API removes the complexity of manual state management, making AI-powered application development faster, more scalable, and highly efficient.

Imagine you’re looking to buy a new car. Traditionally, you would:
✔ Browse Bing for car specifications, pricing, and safety features
✔ Visit official dealership websites
✔ Spend hours researching before heading to a few car lots

But today, Bing’s integration with OpenAI changes everything.

Now, you can simply:
✅ Open Bing’s Chat feature
✅ Enter a specific prompt, such as:
🗨️ “Compare the 2023 Honda CR-V with the 2023 Toyota RAV4.”
✅ Receive detailed, instant results, summarising key features, saving you hours of research

Bing and OpenAI: The Co-Pilot of the Web

This integration brings search and chat together, introducing:
✔ Natural Language Processing (NLP)
✔ Machine Learning (ML)

Bing’s search engine can now:
🔹 Understand complex queries in natural language
🔹 Interpret and respond more effectively
🔹 Provide human-like answers using OpenAI’s language models

Key Features of Bing and OpenAI

1️⃣ Summarised Answers – Bing reviews multiple web results and summarises the key insights so you don’t have to click through multiple links.
2️⃣ Content Composition – Bing can help create content, such as:
🔹 Travel itineraries for your next family trip
🔹 Recipes based on the ingredients you have

The possibilities are endless!

Technical Advancements Behind Bing and OpenAI

Bing now operates on the next-generation OpenAI model, currently ChatGPT-3.5, with ChatGPT-4 already in development.

How Bing Enhances Search with AI:

✔ AI-driven search algorithms deliver more relevant results
✔ A new user experience integrates browsing and chat into one seamless interface
✔ Bing now interacts with APIs, enabling real-time data processing

The Future of AI-Powered Development

With Bing and OpenAI, developers can:
🚀 Innovate new products
🚀 Improve user experiences
🚀 Redefine system architectures faster and more securely

Bing’s AI capabilities transform search into an interactive, intelligent, and efficient experience—saving time, boosting productivity, and unlocking new possibilities for users and developers alike.

Imagine if you could simply give an AI a goal, such as:
📌 “Summarise these five articles into a new document.”
📌 “Create a travel itinerary for our family trip to Norway in December and save it in a spreadsheet.”

Now, imagine the AI figuring out all the steps to achieve that goal without you manually managing the process.

This is exactly what AI agents are designed to do.

How Are AI Agents Different from Traditional AI Systems?

Most AI systems today—such as ChatGPT, Bing, Google Gemini, and Claude—are conversational. You input a prompt, and they return a human-like response.

However, if you have a complex task—like summarising seven articles—you must:
🔹 Copy and paste each article
🔹 Prompt the system for a summary
🔹 Copy the response back into your application

With AI agents, this manual effort is eliminated.

How AI Agents Work

1️⃣ You provide a goal prompt (e.g., “Summarise these five articles”).
2️⃣ The AI agent determines what tasks need to be done.
3️⃣ A task queue is created, breaking the work into steps.
4️⃣ The agent completes each task while retrieving memory to maintain context.
5️⃣ It continues looping through tasks until it meets the original goal.
6️⃣ You receive the final output—without managing each step yourself.

In simple terms, AI agents use multi-step reasoning and task automation to achieve goals more efficiently than traditional AI systems.

Key Features That Make AI Agents Powerful

✔ Long-Term Memory
Traditional AI models (like ChatGPT) have limited context windows, meaning they forget earlier parts of long conversations.
🔹 AI agents solve this by summarising every response, storing it in memory, and using those summaries in future prompts.

✔ Action Execution
Unlike standard AI, AI agents can perform tasks on your behalf, including:
🔹 Searching the internet and downloading files
🔹 Logging into services and filling out forms
🔹 Creating and managing files
🔹 Even installing and configuring software

Are AI Agents Safe?

AI agents are still in their infancy. They are experimental, and their safety depends on how they are used and what access they are given.

Potential risks:
🔹 Unintended actions – The agent may execute tasks you didn’t intend.
🔹 AI hallucinations – As AI systems sometimes generate false information, agents may amplify inaccuracies.
🔹 Security concerns – Granting computer or service access to an AI agent could lead to data loss or unintended system changes.

✔ Are they safe for experimentation? Yes, if you know what you’re doing.
❌ Are they safe for critical work? Not yet.

Should You Use AI Agents Today?

🚫 For general productivity? No—unless you are experimenting.

🚀 For research & development? Maybe—but expect high operational costs and limited real-world utility.

AI agent technology is rapidly evolving, and we will likely see significant improvements in the coming months and years.

The Future of AI Agents

AI agents represent a glimpse into the near future, where goal-oriented AIs not only understand our requests but execute them autonomously.

💡 Projects like Microsoft Copilot and ChatGPT plugins are already introducing context memory, online access, and agent capabilities, moving towards a more sophisticated AI-driven workflow.

While AI agents are not yet ready for critical work, they could soon redefine automation, allowing users to give a goal and let the AI figure out how to accomplish it. The next few years will determine whether AI agents become the standard—or remain an experimental concept. We shall see.

Google has long been a pioneer in AI. From search algorithms to machine learning breakthroughs, it has shaped the digital world. Today, Google is leading the next generation of AI, developing task-specific models that power a wide range of applications.

PaLM: Google’s Advanced Language Model

One of Google’s most well-known AI models is PaLM (Pathways Language Model), currently in its second version (PaLM 2).

What is PaLM 2 great at?
✔ Advanced reasoning tasks
✔ Coding & software development (supports 20+ programming languages)
✔ Mathematical problem-solving
✔ Natural language generation (trained on 100+ spoken languages)

PaLM 2 in Google Products

Google has integrated PaLM 2 into its own applications:

📌 Google Workspace (Docs, Sheets, Gmail) → via Duet AI (Google’s answer to Microsoft’s Copilot)
📌 Google Search → via Search Labs, enhancing search with AI-generated insights
📌 Cybersecurity → via Sec-PaLM, offering AI-powered security protection
📌 Healthcare → via Med-PaLM, trained on medical data, achieving 85.4% accuracy in the US Medical License Exam
📌 Software Development → via Codey, Google’s AI-powered coding assistant (integrated with Android Studio & external editors)

Developers can access PaLM 2 through the PaLM 2 API and MakerSuite, a tool for creating AI-powered applications with Python integration.

Other Google AI Models

🔹 Imagen → A powerful text-to-image diffusion model capable of generating high-resolution, accurate images. It excels at understanding text prompts, rendering text in images, and handling transparency and refraction.

🔹 Chirp → A speech-to-text AI with support for 100+ languages and 98% accuracy in English speech recognition.

🔹 MusicLM → A generative AI model that creates high-fidelity music from text descriptions. It can generate music in different genres, instruments, and moods, even incorporating humming and whistling.

Customisation & Fine-Tuning

Businesses can optimise these models using Google’s Vertex AI, which enables companies to:
✔ Fine-tune AI models with their own data
✔ Improve accuracy for specific applications
✔ Develop AI-powered business solutions

The Future of Google AI

While Google has been pioneering AI for years, the pace of innovation has accelerated dramatically. With cutting-edge models like PaLM, Imagen, Chirp, and MusicLM, Google is reshaping AI applications across search, productivity, security, healthcare, and creative fields.

And this is just the beginning.

The future of Google AI looks incredibly promising.

Since its launch in June 2022, GitHub Copilot has seen significant upgrades, including a business version. Here’s everything you need to know about its impact and improvements.

Copilot’s Growing Influence

According to GitHub’s research and usage data:

✔ 46% of code across all programming languages is now AI-generated by Copilot.
✔ 61% of Java developers rely on Copilot for code generation.
✔ 90% of developers report completing tasks faster.
✔ 75% say it helps them focus on more satisfying and meaningful work.

The goal? To make coding more efficient and enjoyable.

Technical Upgrades: Smarter & More Context-Aware

🚀 Improved OpenAI Codex → The AI model behind Copilot has been enhanced for better code predictions.
🚀 “Fill in the Middle” (FIM) Model → Instead of looking only before the insertion point, Copilot now analyses code before and after, providing more context-aware suggestions.
🚀 Personalised Lightweight Model → Copilot adapts to your preferences, learning from which suggestions you accept or reject for more accurate recommendations.
🚀 Stronger Security → GitHub has enhanced Copilot’s ability to detect and prevent insecure code suggestions.

GitHub Copilot for Business

GitHub now offers a business plan with enterprise-level features, including:

✔ License & policy management for teams
✔ Proxy & corporate VPN support
✔ Increased privacy – Businesses can use Copilot without storing their code on GitHub
✔ Editor integrations – Works with JetBrains, Visual Studio, and Neovim

💰 Pricing: $19 per seat (almost double the individual plan)

The Future of AI-Assisted Coding

With AI now generating nearly half of all code, tools like GitHub Copilot are shaping the future of software development. It’s faster, smarter, and more intuitive—and with ongoing improvements, it’s becoming an indispensable tool for developers and businesses alike. AI-powered coding is here to stay.

AI is not the future; it’s the present shaping tomorrow.