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Generative AI

OpenAI model series, o1, marks a turning point in AI development, setting a new standard for how machines approach complex problems. Unlike its predecessors, which excelled in generating fluent language and basic reasoning, the o1 models were designed to think step-by-step, making them significantly better at tackling intricate tasks like coding and advanced mathematics.

What makes the OpenAI model, o1 stand out? It’s not just about size or speed—it’s about their unique ability to process information in a more human-like, logical sequence. This breakthrough promises to reshape what’s possible with AI, pushing the boundaries of accuracy and reliability. Curious about how these models are redefining the future of artificial intelligence? Read on to discover what makes them truly groundbreaking.

 

What is o1? Decoding the Hype Around the New OpenAI Model

The OpenAI o1 model series, which includes o1-preview and o1-mini, marks a significant evolution in the development of artificial intelligence. Unlike earlier models like GPT-4, which were optimized primarily for language generation and basic reasoning, o1 was designed to handle more complex tasks by simulating human-like step-by-step thinking.

This model series was developed to excel in areas where precision and logical reasoning are crucial, such as advanced mathematics, coding, and scientific analysis.

Key Features of OpenAI o1

  1. Chain-of-Thought Reasoning:  A key innovation in the o1 series is its use of chain-of-thought reasoning, which enables the model to think through problems in a sequential manner. This involves processing a series of intermediate steps internally, which helps the model arrive at a more accurate final answer.
    For instance, when solving a complex math problem, the OpenAI o1 model doesn’t just generate an answer; it systematically works through the formulas and calculations, ensuring a more reliable result.
  2. Reinforcement Learning with Human Feedback: Unlike earlier models, o1 was trained using reinforcement learning with human feedback (RLHF), which means the model received rewards for generating desired reasoning steps and aligning its outputs with human expectations.
    This approach not only enhances the model’s ability to perform intricate tasks but also improves its alignment with ethical and safety guidelines. This training methodology allows the model to reason about its own safety protocols and apply them in various contexts, thereby reducing the risk of harmful or biased outputs.
  3. A New Paradigm in Compute Allocation: The OpenAI o1 model stands out by reallocating computational resources from massive pretraining datasets to the training and inference phases. This shift enhances the model’s complex reasoning abilities.

     

    How Compute Increases Reasoning Abilities of openai model o1 in the inference stage
    Source: OpenAI

     

    The provided chart illustrates that increased compute, especially during inference, significantly boosts the model’s accuracy in solving AIME math problems. This suggests that more compute allows o1 to “think” more effectively, highlighting its compute-intensive nature and potential for further gains with additional resources.

  4. Reasoning Tokens: To manage complex reasoning internally, the o1 models use “reasoning tokens”. These tokens are processed invisibly to users but play a critical role in allowing the model to think through intricate problems. By using these internal markers, the model can maintain a clear and concise output while still performing sophisticated computations behind the scenes.
  5. Extended Context Window: The o1 models offer an expanded context window of up to 128,000 tokens. This capability enables the model to handle longer and more complex interactions, retaining much more information within a single session. It’s particularly useful for working with extensive documents or performing detailed code analysis.
  6. Enhanced Safety and Alignment: Safety and alignment have been significantly improved in the o1 series. The models are better at adhering to safety protocols by reasoning through these rules in real-time, reducing the risk of generating harmful or biased content. This makes them not only more powerful but also safer to use in sensitive applications.

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Performance of o1 Vs. GPT-4o; Comparing the Latest OpenAI Models

The OpenAI o1 series showcases significant improvements in reasoning and problem-solving capabilities compared to previous models like GPT-4o.

 

Here’s a complete guide to understanding LLM evaluation

 

Here’s a detailed look at how o1 outperforms its predecessors across various domains:

1. Advanced Reasoning and Mathematical Benchmarks:

The o1 models excel in complex reasoning tasks, significantly outperforming GPT-4o in competitive math challenges. For example, in a qualifying exam for the International Mathematics Olympiad (IMO), the o1 model scored 83%, while GPT-4o only managed 13%.

This indicates a substantial improvement in handling high-level mathematical problems and suggests that the o1 models can perform on par with PhD-level experts in fields like physics, chemistry, and biology.

 

OpenAI o1 Performance in coding, math and PhD level questions

 

2. Competitive Programming and Coding:

The OpenAI o1 models also show superior results in coding tasks. They rank in the 89th percentile on platforms like Codeforces, indicating their ability to handle complex coding problems and debug efficiently. This performance is a marked improvement over GPT-4o, which, while competent in coding, does not achieve the same level of proficiency in competitive programming scenarios.

 

OpenAI o1 Vs. GPT-4o - In Coding

 

Read more about Top AI Tools for Code Generation

 

3. Human Evaluations and Safety:

In human preference tests, o1-preview consistently received higher ratings for tasks requiring deep reasoning and complex problem-solving. The integration of “chain of thought” reasoning into the model enhances its ability to manage multi-step reasoning tasks, making it a preferred choice for more complex applications.

Additionally, the o1 models have shown improved performance in handling potentially harmful prompts and adhering to safety protocols, outperforming GPT-4o in these areas.

 

o1 Vs. GPT-4o in terms of human preferences

 

Explore more about Evaluating Large Language Models

 

4. Standard ML Benchmarks:

On standard machine learning benchmarks, the OpenAI o1 models have shown broad improvements across the board. They have demonstrated robust performance in general-purpose tasks and outperformed GPT-4o in areas that require nuanced understanding and deep contextual analysis. This makes them suitable for a wide range of applications beyond just mathematical and coding tasks.

 

o1 Vs. GPT-4o in terms of ML benchmarks

 

Use Cases and Applications of OpenAI Model, o1

Models like OpenAI’s o1 series are designed to excel in a range of specialized and complex tasks, thanks to their advanced reasoning capabilities. Here are some of the primary use cases and applications:

1. Advanced Coding and Software Development:

The OpenAI o1 models are particularly effective in complex code generation, debugging, and algorithm development. They have shown proficiency in coding competitions, such as those on Codeforces, by accurately generating and optimizing code. This makes them valuable for developers who need assistance with challenging programming tasks, multi-step workflows, and even generating entire software solutions.

 

Learn how LLMs can be used for code generation

 

2. Scientific Research and Analysis:

With their ability to handle complex calculations and logic, OpenAI o1 models are well-suited for scientific research. They can assist researchers in fields like chemistry, biology, and physics by solving intricate equations, analyzing data, and even suggesting experimental methodologies. They have outperformed human experts in scientific benchmarks, demonstrating their potential to contribute to advanced research problems.

3. Legal Document Analysis and Processing:

In legal and professional services, the OpenAI o1 models can be used to analyze lengthy contracts, case files, and legal documents. They can identify subtle differences, summarize key points, and even assist in drafting complex documents like SPAs and S-1 filings, making them a powerful tool for legal professionals dealing with extensive and intricate paperwork.

4. Mathematical Problem Solving:

The OpenAI o1 models have demonstrated exceptional performance in advanced mathematics, solving problems that require multi-step reasoning. This includes tasks like calculus, algebra, and combinatorics, where the model’s ability to work through problems logically is a major advantage. They have achieved high scores in competitions like the American Invitational Mathematics Examination (AIME), showing their strength in mathematical applications.

 

Read more about the key statistical distributions to know

 

5. Education and Tutoring:

With their capacity for step-by-step reasoning, o1 models can serve as effective educational tools, providing detailed explanations and solving complex problems in real-time. They can be used in educational platforms to tutor students in STEM subjects, help them understand complex concepts, and guide them through difficult assignments or research topics.

6. Data Analysis and Business Intelligence:

The ability of o1 models to process large amounts of information and perform sophisticated reasoning makes them suitable for data analysis and business intelligence. They can analyze complex datasets, generate insights, and even suggest strategic decisions based on data trends, helping businesses make data-driven decisions more efficiently.

These applications highlight the versatility and advanced capabilities of the o1 models, making them valuable across a wide range of professional and academic domains.

 

How generative AI and LLMs work

 

Limitations of o1

Despite the impressive capabilities of OpenAI’s o1 models, they do come with certain limitations that users should be aware of:

1. High Computational Costs:

The advanced reasoning capabilities of the OpenAI o1 models, including their use of “reasoning tokens” and extended context windows, make them more computationally intensive compared to earlier models like GPT-4o. This results in higher costs for processing and slower response times, which can be a drawback for applications that require real-time interactions or large-scale deployment.

2. Limited Availability and Access:

Currently, the o1 models are only available to a select group of users, such as those with API access through specific tiers or ChatGPT Plus subscribers. This restricted access limits their usability and widespread adoption, especially for smaller developers or organizations that may not meet the requirements for access.

3. Lack of Transparency in Reasoning:

While the o1 models are designed to reason through complex problems using internal reasoning tokens, these intermediate steps are not visible to the user. This lack of transparency can make it challenging for users to understand how the model arrives at its conclusions, reducing trust and making it difficult to validate the model’s outputs, especially in critical applications like healthcare or legal analysis.

4. Limited Feature Support:

The current o1 models do not support some advanced features available in other models, such as function calling, structured outputs, streaming, and certain types of media integration. This limits their versatility for applications that rely on these features, and users may need to switch to other models like GPT-4o for specific use cases.

 

Dig deeper into understanding GPT-4o

 

5. Higher Risk in Certain Applications:

Although the o1 models have improved safety mechanisms, they still pose a higher risk in certain domains, such as generating biological threats or other sensitive content. The complexity and capability of the model can make it more difficult to predict and control its behavior in risky scenarios, despite the improved alignment efforts.

6. Incomplete Implementation:

As the o1 models are currently in a preview state, they lack several planned features, such as support for different media types and enhanced safety functionalities. This incomplete implementation means that users may experience limitations in functionality and performance until these features are fully developed and integrated into the models.

In summary, while the o1 models offer groundbreaking advancements in reasoning and problem-solving, they are accompanied by challenges such as high computational costs, limited availability, lack of transparency in reasoning, and some missing features that users need to consider based on their specific use cases.

 

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Final Thoughts: A Step Forward with Limitations

The OpenAI o1 model series represents a remarkable advancement in AI, with its ability to perform complex reasoning and handle intricate tasks more effectively than its predecessors. Its unique focus on step-by-step problem-solving has opened new possibilities for applications in coding, scientific research, and beyond.

However, these capabilities come with trade-offs. High computational costs, limited access, and incomplete feature support mean that while o1 offers significant benefits, it’s not yet a one-size-fits-all solution.

As OpenAI continues to refine and expand the o1 series, addressing these limitations will be crucial for broader adoption and impact. For now, o1 remains a powerful tool for those who can leverage its advanced reasoning capabilities, while also navigating its current constraints.

September 19, 2024

In today’s world, data is exploding at an unprecedented rate, and the challenge is making sense of it all.

Generative AI (GenAI) is stepping in to change the game by making data analytics accessible to everyone.

Imagine asking a question in plain English and instantly getting a detailed report or a visual representation of your data—this is what GenAI can do.

 

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It’s not just for tech experts anymore; GenAI democratizes data science, allowing anyone to extract insights from data easily.

As data keeps growing, tools powered by Generative AI for data analytics are helping businesses and individuals tap into this potential, making decisions faster and smarter.

How is Generative AI Different from Traditional AI Models?

Traditional AI models are designed to make decisions or predictions within a specific set of parameters. They classify, regress, or cluster data based on learned patterns but do not create new data.

In contrast, generative AI can handle unstructured data and produce new, original content, offering a more dynamic and creative approach to problem-solving.

For instance, while a traditional AI model might predict the next word in a sentence based on prior data, a generative AI model can write an entire paragraph or create a new image from scratch.

Also read about GenAI in people operations

Generative AI for Data Analytics – Understanding the Impact

To understand the impact of generative AI for data analytics, it’s crucial to dive into the underlying mechanisms, that go beyond basic automation and touch on complex statistical modeling, deep learning, and interaction paradigms.

1. Data Generation and Augmentation

Generative AI models like Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs) are capable of learning the underlying distribution of a dataset. They generate new data points that are statistically similar to the original data.

Impact on Data Analytics:

  • Data Imbalance: GenAI can create synthetic minority class examples to balance datasets, improving the performance of models trained on these datasets.

 

A detailed guide on data augmentation

 

  • Scenario Simulation: In predictive modeling, generative AI can create various future scenarios by generating data under different hypothetical conditions, allowing analysts to explore potential outcomes in areas like risk assessment or financial forecasting.

2. Pattern Recognition and Anomaly Detection

Generative models, especially those based on probabilistic frameworks like Bayesian networks, can model the normal distribution of data points. Anomalies are identified when new data deviates significantly from this learned distribution. This process involves estimating the likelihood of a given data point under the model and flagging those with low probabilities.

Impact on Data Analytics:

  • Fraud Detection: In financial data, generative models can identify unusual transactions by learning what constitutes “normal” behavior and flagging deviations.

 

Another interesting read: FraudGPT

 

  • Predictive Maintenance: In industrial settings, GenAI can identify equipment behaviors that deviate from the norm, predicting failures before they occur.

3. Natural Language Processing (NLP) for Data Interaction

Generative AI models like GPT-4 utilize transformer architectures to understand and generate human-like text based on a given context. These models process vast amounts of text data to learn language patterns, enabling them to respond to queries, summarize information, or even generate complex SQL queries based on natural language inputs.

Impact on Data Analytics:

  • Accessibility: NLP-driven generative AI enables non-technical users to interact with complex datasets using plain language, breaking down barriers to data-driven decision-making.

 

Explore more: Generative AI for Data Analytics: A Detailed Guide

 

  • Automation of Data Queries: Generative AI can automate the process of data querying, enabling quicker access to insights without requiring deep knowledge of SQL or other query languages.

4. Automated Insights and Report Generation

Generative AI can process data and automatically produce narratives or insights by interpreting patterns within the data. This is done using models trained to generate text based on statistical analysis, identifying key trends, outliers, and patterns without human intervention.

Impact on Data Analytics:

  • Efficiency: Automating the generation of insights saves time for analysts, allowing them to focus on strategic decision-making rather than routine reporting.

  • Personalization: Reports can be tailored to different audiences, with generative AI adjusting the complexity and focus based on the intended reader.

5. Predictive Modeling and Simulation

Generative AI can simulate various outcomes by learning from historical data and predicting future data points. This involves using models like Bayesian networks, Monte Carlo simulations, or deep generative models to create possible future scenarios based on current trends and data.

Impact on Data Analytics:

  • Risk Management: By simulating various outcomes, GenAI helps organizations prepare for potential risks and uncertainties.

  • Strategic Planning: Predictive models powered by generative AI enable businesses to explore different strategic options and their likely outcomes, leading to more informed decision-making.

 

Learn to build a predictive model

 

Key Tools and Platforms for AI Data Analytics

Generative AI tools for data analytics can automate complex processes, generate insights, and enhance user interaction with data.

Below is a more detailed exploration of notable tools that leverage generative AI for data analytics, diving into their core mechanisms, features, and applications.

Top 7 Generative AI tools for Data Analytics

1. Microsoft Power BI with Copilot

Microsoft Power BI has integrated genAI through its Copilot feature, transforming how users interact with data. The Copilot in Power BI allows users to generate reports, visualizations, and insights using natural language queries, making advanced analytics accessible to a broader audience.

Core Mechanism:

  • Natural Language Processing (NLP): The Copilot in Power BI is powered by sophisticated NLP models that can understand and interpret user queries written in plain English. This allows users to ask questions about their data and receive instant visualizations and insights without needing to write complex queries or code.

  • Generative Visualizations: The AI generates appropriate visualizations based on the user’s query, automatically selecting the best chart types, layouts, and data representations to convey the requested insights.

  • Data Analysis Automation: Beyond generating visualizations, the Copilot can analyze data trends, identify outliers, and suggest next steps or further analysis. This capability automates much of the manual work traditionally involved in data analytics.

 

How generative AI and LLMs work

 

Features:

  • Ask Questions with Natural Language: Users can type questions directly into the Power BI interface, such as “What were the sales trends last quarter?” and the Copilot will generate a relevant chart or report.

  • Automated Report Creation: Copilot can automatically generate full reports based on high-level instructions, pulling in relevant data sources, and organizing the information in a coherent and visually appealing manner.

  • Insight Suggestions: Copilot offers proactive suggestions, such as identifying anomalies or trends that may require further investigation, and recommends actions based on the data analysis.

Applications:

  • Business Intelligence: Power BI’s Copilot is especially valuable for business users who need to quickly derive insights from data without having extensive technical knowledge. It democratizes access to data analytics across an organization.

  • Real-time Data Interaction: The Copilot feature enhances real-time interaction with data, allowing for dynamic querying and immediate feedback, which is crucial in fast-paced business environments.

2. Tableau Pulse

Tableau Pulse is a new feature in Tableau’s data analytics platform that integrates generative AI to make data analysis more intuitive and personalized. It delivers insights directly to users in a streamlined, accessible format, enhancing decision-making without requiring deep expertise in analytics.

Core Mechanism of Tableau Pulse:

  • AI-Driven Insights: Tableau Pulse uses AI to generate personalized insights, continuously monitoring data to surface relevant trends and anomalies tailored to each user’s needs.
  • Proactive Notifications: Users receive timely, context-rich notifications, ensuring they are always informed of important changes in their data.
The Architecture of Tableau Pulse
Source: Tableau

Detailed Features of Tableau Pulse:

  • Contextual Analysis: Provides explanations and context for highlighted data points, offering actionable insights based on current trends.
  • Interactive Dashboards: Dashboards dynamically adjust to emphasize the most relevant data, simplifying the decision-making process.

Applications:

  • Real-Time Decision Support: Ideal for fast-paced environments where immediate, data-driven decisions are crucial.
  • Operational Efficiency: Automates routine analysis, allowing businesses to focus on strategic goals with less manual effort.
  • Personalized Reporting: Perfect for managers and executives who need quick, relevant updates on key metrics without delving into complex data sets.

3. DataRobot

DataRobot is an end-to-end AI and machine learning platform that automates the entire data science process, from data preparation to model deployment. The platform’s use of generative AI enhances its ability to provide predictive insights and automate complex analytical processes.

Core Mechanism:

  • AutoML: DataRobot uses generative AI to automate the selection, training, and tuning of machine learning models. It generates a range of models and ranks them based on performance, making it easy to identify the best approach for a given dataset.

  • Insight Generation: DataRobot’s AI can automatically generate insights from data, identifying important variables, trends, and potential predictive factors that users may not have considered.

Detailed Features:

  • Model Explainability: DataRobot provides detailed explanations for its models’ predictions, using techniques like SHAP values to show how different factors contribute to outcomes.

  • Time Series Forecasting: The platform can generate and test time series models, predicting future trends based on historical data with minimal input from the user.

Applications:

  • Customer Analytics: DataRobot is commonly used for customer behavior prediction, helping businesses optimize their marketing strategies based on AI-generated insights.

  • Predictive Maintenance: The platform is widely used in industrial settings to predict equipment failures before they occur, minimizing downtime and maintenance costs.

4. Qlik

Qlik has incorporated generative AI through its Qlik Answers assistant, transforming how users interact with data. Qlik Answers allows users to embed generative AI analytics content into their reports and dashboards, making data analytics more intuitive and accessible.

Features:

  • Ask Questions with Natural Language: Users can type questions directly into the Qlik interface, such as “What are the key sales trends this year?” and Qlik Answers will generate relevant charts, summaries, or reports.
  • Automated Summaries: Qlik Answers provides automated summaries of key data points, making it easier for users to quickly grasp important information without manually sifting through large datasets.
  • Natural Language Reporting: The platform supports natural language reporting, which means it can create reports and dashboards in plain English, making the information more accessible to users without technical expertise.

 

Explore a hands-on curriculum that helps you build custom LLM applications!

 

Applications:

  • Business Intelligence: Qlik Answers is particularly valuable for business users who need to derive insights quickly from large volumes of data, including unstructured data like text or videos. It democratizes access to data analytics across an organization, enabling more informed decision-making.
  • Real-time Data Interaction: The natural language capabilities of Qlik Answers enhance real-time interaction with data, allowing for dynamic querying and immediate feedback. This is crucial in fast-paced business environments where timely insights can drive critical decisions.

These features and capabilities make Qlik a powerful tool for businesses looking to leverage generative AI to enhance their data analytics processes, making insights more accessible and actionable.

5. SAS Viya

SAS Viya is an AI-driven analytics platform that supports a wide range of data science activities, from data management to model deployment. The integration of generative AI enhances its capabilities in predictive analytics, natural language interaction, and automated data processing.

Core Mechanism:

  • AutoAI for Model Building: SAS Viya’s AutoAI feature uses generative AI to automate the selection and optimization of machine learning models. It can generate synthetic data to improve model robustness, particularly in scenarios with limited data.

  • NLP for Data Interaction: SAS Viya enables users to interact with data through natural language queries, with generative AI providing insights and automating report generation based on these interactions.

Detailed Features:

  • In-memory Analytics: SAS Viya processes data in-memory, which allows for real-time analytics and the rapid generation of insights using AI.

  • AI-Powered Data Refinement: The platform includes tools for automating data cleansing and transformation, making it easier to prepare data for analysis.

Applications:

  • Risk Management: SAS Viya is widely used in finance to model and manage risk, using AI to simulate various risk scenarios and their potential impact.

  • Customer Intelligence: The platform helps businesses analyze customer data, segment markets, and optimize customer interactions based on AI-driven insights.

6. Alteryx

Alteryx is designed to make data analytics accessible to both technical and non-technical users by providing an intuitive interface and powerful tools for data blending, preparation, and analysis. Generative AI in Alteryx automates many of these processes, allowing users to focus on deriving insights from their data.

Core Mechanism:

  • Automated Data Preparation: Alteryx uses generative AI to automate data cleaning, transformation, and integration, which reduces the manual effort required to prepare data for analysis.

  • AI-Driven Insights: The platform can automatically generate insights by analyzing the underlying data, highlighting trends, correlations, and anomalies that might not be immediately apparent.

Detailed Features:

  • Visual Workflow Interface: Alteryx’s drag-and-drop interface is enhanced by AI, which suggests optimizations and automates routine tasks within data workflows.

  • Predictive Modeling: The platform offers a suite of predictive modeling tools that use generative AI to forecast trends, identify key variables, and simulate different scenarios.

Applications:

  • Marketing Analytics: Alteryx is often used to analyze and optimize marketing campaigns, predict customer behavior, and allocate marketing resources more effectively.

  • Operational Efficiency: Businesses use Alteryx to optimize operations by analyzing process data, identifying inefficiencies, and recommending improvements based on AI-generated insights.

7. H2O.ai

H2O.ai is a powerful open-source platform that automates the entire data science process, from data preparation to model deployment. It enables businesses to quickly build, tune, and deploy machine learning models without needing deep technical expertise.

Key Features:

  • AutoML: Automatically selects the best models, optimizing them for performance.
  • Model Explainability: Provides transparency by showing how predictions are made.
  • Scalability: Handles large datasets, making it suitable for enterprise-level applications.

Applications: H2O.ai is widely used for predictive analytics in various sectors, including finance, healthcare, and marketing. It empowers organizations to make data-driven decisions faster, with more accuracy, and at scale.

Real-World Applications and Use Cases

Generative AI has found diverse and impactful applications in data analytics across various industries. These applications leverage the ability of GenAI to process, analyze, and generate data, enabling more efficient, accurate, and innovative solutions to complex problems. Below are some real-world applications of GenAI in data analytics:

  1. Customer Personalization: E-commerce platforms like Amazon use GenAI to analyze customer behavior and generate personalized product recommendations, enhancing user experience and engagement.

    Explore: AI powered marketing

  2. Fraud Detection: Financial institutions utilize GenAI to detect anomalies in transaction patterns, helping prevent fraud by generating real-time alerts for suspicious activities.

  3. Predictive Maintenance: Companies like Siemens use GenAI to predict equipment failures by analyzing sensor data, allowing for proactive maintenance and reduced downtime.

  4. Healthcare Diagnostics: AI-driven tools in healthcare analyze patient data to assist in diagnosis and personalize treatment plans, as seen in platforms like IBM Watson Health.

    Explore the role of AI in healthcare.

  5. Supply Chain Optimization: Retailers like Walmart leverage GenAI to forecast demand and optimize inventory, improving supply chain efficiency.

  6. Content Generation: Media companies such as The Washington Post use GenAI to generate articles, while platforms like Spotify personalize playlists based on user preferences.

  7. Anomaly Detection in IT: IT operations use GenAI to monitor systems for security breaches or failures, automating responses to potential threats.

  8. Financial Forecasting: Hedge funds utilize GenAI for predicting stock prices and managing financial risks, enhancing decision-making in volatile markets.

    Learn how GenAI is reshaping the future of finance

  9. Human Resources: Companies like Workday use GenAI to optimize hiring, performance evaluations, and workforce planning based on data-driven insights.

  10. Environmental Monitoring: Environmental agencies monitor climate change and pollution using GenAI to generate forecasts and guide sustainability efforts.

These applications highlight how GenAI enhances decision-making, efficiency, and innovation across various sectors.

Start Leveraging Generative AI for Data Analytics Today

Generative AI is not just a buzzword—it’s a powerful tool that can transform how you analyze and interact with data. By integrating GenAI into your workflow, you can make data-driven decisions more efficiently and effectively.

August 16, 2024

Artificial intelligence (AI) has infiltrated every field of life, creating new avenues of development and creativity. Amongst these advancements is AI music generation. It refers to the use of AI tools and models to create melodious notes.

However, it is a complex process as generating music is challenging and requires modeling long-range sequences. Unlike speech, music requires the full frequency spectrum [Müller, 2015]. That means sampling the signal at a higher rate, i.e., the standard sampling rates of music recordings are 44.1 kHz or 48 kHz vs. 16 kHz for speech.

Moreover, the music contains harmonies and melodies from different instruments, creating complex structures. Since human listeners are highly sensitive to disharmony [Fedorenko et al., 2012; Norman-Haignere et al., 2019], generating music does not leave much room for making melodic errors.

 

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Hence, the ability to control the generation process in a diverse set of methods, e.g., key, instruments, melody, genre, etc. is essential for music creators. Today, music generation models powered by AI are designed to cater to these complexities and promote creativity.

In this blog, we will explore the 5 leading AI music generation models and their role in revamping the music industry. Before we navigate the music generation models, let’s dig deeper into the idea of AI generated music and what it actually means.

What is AI Music Generation?

It is the process of using AI to generate music. It can range from composing entire pieces to assisting with specific elements like melodies or rhythms. AI analyzes large datasets of music, from catchy pop tunes to timeless symphonies, to learn the basics of music generation.

This knowledge lets it create new pieces based on your preferences. You can tell the AI what kind of music you want (think rock ballad or funky disco) and even provide starting ideas. Using its knowledge base and your input, AI generates melodies, harmonies, and rhythms. Some tools even allow you to edit the outputs as needed.

As a result, the music generation process has become more interesting and engaging. Some benefits of AI generated music include:

 

Explore the top 7 AI Music Generators

 

Enhanced Creativity and Experimentation

AI tools empower musicians to experiment with different styles and rhythms. It also results in the streamlining of the song production process, allowing for quick experimentation with new sounds and ideas.

This allows for the creation of personalized music based on individual preferences and moods can revolutionize how we listen to music. This capability enables the generation of unique soundtracks tailored to daily activities or specific emotional states.

Accessibility and Democratization

AI music generation tools make music creation accessible to everyone, regardless of their musical background or technical expertise. These tools enable users to compose music through text input, democratizing music production.

Moreover, in educational settings, AI tools introduce students to the fundamentals of music composition, allowing them to learn and create music in an engaging way. This practical approach helps cultivate musical skills from a young age.

Efficiency and Quality

AI music tools simplify the music-making process, allowing users to quickly craft complete songs without compromising quality. This efficiency is particularly beneficial for professional musicians and production teams.

Plus, AI platforms ensure that the songs produced are of professional-grade audio quality. This high level of sound clarity and richness ensures that AI-generated music captures and holds the listener’s attention.

 

Learn about AI tools for code generation

 

Cost and Time Savings

These tools also significantly reduce the costs associated with traditional music production, including studio time and hiring session musicians. This makes it an attractive option for indie artists and small production houses. Hence, music can be generated quickly and at lower costs.

These are some of the most common advantages of utilizing AI in music generation. While we understand the benefits, let’s take a closer look at the models involved in the process.

 

 

Also learn about AI tools that could revolutionize your daily routine

 

Types of Music Generation Models

There are two main types of music generation models utilized to create AI music.

1. Autoregressive Models

 

Autoregressive model architecture
Overview of the architecture of an Autoregressive Model – Source: ResearchGate

 

These models are a fundamental approach in AI music generation, where they predict future elements of a sequence based on past elements. They generate data points in a sequence one at a time, using previous data points to inform the next.

In the context of music generation, this means predicting the next note or sound based on the preceding ones. The model is trained to understand the sequence patterns and dependencies in the musical data. This makes them particularly effective for tasks involving sequence generation like music.

Thus, autoregressive models can generate high-quality, coherent musical compositions that align well with provided text descriptions or melodies. However, they are computationally complex, making their cost a challenge as each token prediction depends on all previous tokens, leading to higher inference times for long sequences.

2. Diffusion Models

 

Stable Audio 2.0 architecture
Overview of the Stable Audio 2.0 architecture – Source: stability.ai

 

They are an emerging class of generative models that have shown promising results in various forms of data generation, including music. These models work by reversing a diffusion process, which gradually adds noise to the data, and then learning to reverse this process to generate new data.

Diffusion models can be applied to generate music by treating audio signals as the data to be diffused and denoised. Here’s how they are typically employed:

  1. Audio Representation: Music is represented in a compressed form, such as spectrograms or latent audio embeddings, which are then used as the input to the diffusion process.
  2. Noise Addition: Gaussian noise is added to these representations over several steps, creating a series of increasingly noisy versions of the original music.
  3. Model Training: A neural network is trained to reverse the noise addition process. This involves learning to predict the original data from the noisy versions at each step.
  4. Music Generation: During generation, the model starts with pure noise and applies the learned reverse process to generate new music samples.

Thus, diffusion models can generate high-quality audio with fine details. They are flexible as they can handle various conditioning inputs, such as text descriptions or reference audio, making them versatile for different music generation tasks. However, they also pose the challenge of high computational costs.

 

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5 Leading Music Generation Models

Now that we understand the basic models used in AI music generation, it is time we explore the 5 leading music generation models in the market nowadays.

 

Top 5 Music Generation Models

 

1. MusicLM by Google

MusicLM is an AI music system developed by Google to create music based on textual prompts. It allows users to specify the genre, mood, instruments, and overall feeling of the desired music through words. Once a user inputs their prompt, the tool will generate multiple versions of the request.

Moreover, the tool allows the users to refine the outputs by specifying instruments and the desired effect or emotion. Google also published an academic paper to highlight the different aspects of its AI tool for music generation.

 

Training and inference of MusicLM by Google
Training and inference of MusicLM by Google – Source: arXiv

 

While you can explore the paper at leisure, here is a breakdown of how MusicLM works:

  1. Training Data:
  2. Token-Based Representation:
    • The system models sound in three distinct aspects: the correspondence between words and music, large-scale composition, and small-scale details.
    • Different types of tokens are used to represent these aspects:
      • Audio-Text Tokens: Generated by MuLan, a transformer-based system pre-trained on soundtracks of 44 million online music videos, these tokens capture the relationship between music and its descriptions.
      • Semantic Tokens: Produced by w2v-BERT, these tokens represent large-scale compositions and are fine-tuned on 8,200 hours of music.
      • Acoustic Tokens: Created by a SoundStream autoencoder, these tokens capture small-scale details of the music and are also fine-tuned on 8,200 hours of music.
  3. Transformation and Generation:
    • Given a text description, MuLan generates audio-text tokens, which are then used to guide the generation of semantic tokens by a series of transformers.
    • Another series of transformers takes these semantic tokens and generates acoustic tokens, which are then decoded by the SoundStream decoder to produce the final music clip.
  4. Inference Process:
    • During inference, the model starts with audio-text tokens generated from the input description. These tokens then undergo a series of transformations and decoding steps to generate a music clip.

Evaluation and Performance

  • The authors evaluated MusicLM on 1,000 text descriptions from a text-music dataset, comparing it to two other models, Riffusion and Mubert. MusicLM was judged to have created the best match 30.0% of the time, compared to 15.2% for Riffusion and 9.3% for Mubert 1.

MusicLM is a significant advancement in AI-driven music generation. It is available in the AI Test Kitchen app on the web, Android, or iOS, where users can generate music based on their text inputs. To avoid legal challenges, Google has restricted this available version, preventing it from generating music with specific artists or vocals.

 

How generative AI and LLMs work

 

2. MusicGen by Meta

MusicGen by Meta is an advanced AI model designed for music generation based on text descriptions or existing melodies. It is built on a robust transformer model and employs various techniques to ensure high-quality music generation.

This is similar to how language models predict the next words in a sentence. The model employs an audio tokenizer called EnCodec to break down audio data into smaller parts for easier processing.

 

EnCodec architecture
EnCodec architecture forms the basis for MusicGen – Source: arXiv

 

Some key components and aspects of MusicGen are as follows:

  1. Training Dataset:
    • The model was trained on a large dataset of 20,000 hours of music. This includes 10,000 high-quality licensed music tracks and 390,000 instrument-only tracks from stock media libraries such as Shutterstock and Pond5. This extensive dataset ensures that MusicGen can generate tunes that resonate well with listeners.
  2. Residual Vector Quantization (RVQ):
    • MusicGen leverages RVQ, a multi-stage quantization method that reduces data usage while maintaining high-quality audio output. This technique involves using multiple codebooks to quantize the audio data iteratively, thereby achieving efficient data compression and high fidelity.
  3. Model Architecture:
    • The architecture comprises an encoder, decoder, and conditioning modules. The encoder converts input audio into a vector representation, which is then quantized using RVQ. The decoder reconstructs the audio from these quantized vectors. The conditioning modules handle text or melody inputs, allowing the model to generate music that aligns with the provided prompts.
  4. Open Source:
    • Meta has open-sourced MusicGen, including the code and pre-trained models. This allows researchers and developers to reproduce the results and contribute to further improvements.

Performance and Evaluation

  • MusicGen produces reasonably melodic and coherent music, especially for basic prompts. It has been noted to perform on par or even outshine other AI music generators like Google’s MusicLM in terms of musical coherence for complex prompts.

Hence, MusicGen offers a blend of creativity and technical precision within the world of music generation. Its ability to use both text and melody prompts, coupled with its open-source nature, makes it a valuable tool for researchers, musicians, and AI enthusiasts alike.

 

Also learn how to use AI image generation tools 

 

3. Suno AI

Suno AI is an innovative AI-powered tool designed to democratize music creation by enabling users to compose music through text input. It leverages AI to translate users’ ideas into musical outputs. Users can input information in the textual data, including the mood of your song or the lyrics you have written.

 

Suno AI - AI Music
A quick glance at Suno AI

 

The algorithms craft melodies and harmonies that align with the users’ input information. It results in structured and engaging melodious outputs. The AI refines every detail of the output song, from lyrics to rhythm, resulting in high-quality music tracks that capture your creative spark.

Moreover, the partnership with Microsoft Copilot enhances Suno AI’s capabilities, broadening creative horizons and transforming musical concepts into reality. It is a user-friendly platform with a simplified music-making process, ensuring enhanced accessibility and efficiency.

Some top features of Suno AI are listed below.

  • High-Quality Instrumental Tracks: Suno AI creates high-quality instrumental tracks that align perfectly with the song’s theme and mood, ranging from soft piano melodies to dynamic guitar riffs.
  • Exceptional Audio Quality: Every song produced boasts professional-grade audio quality, ensuring clarity and richness that captures and holds attention.
  • Flexibility and Versatility: The platform adapts to a wide range of musical styles and genres, making it suitable for various types of music creation, from soothing ballads to upbeat dance tracks.

Users can start using Suno AI by signing up for the platform, providing text input, and letting Suno AI generate a unique composition based on their input. The platform offers a straightforward and enjoyable music creation experience.

4. Project Music GenAI Control by Adobe

Project Music GenAI Control by Adobe is an innovative tool designed to revolutionize the creation and editing of custom audio and music. It allows users to share textual prompts to generate music pieces. Once generated, it provides users fine-grained control to edit the audio to their needs.

 

 

The editing options include:

  • Adjusting the tempo, structure, and repeating patterns of the music.
  • Modifying the intensity of the audio at specific points.
  • Extending the length of a music clip.
  • Re-mixing sections of the audio.
  • Creating seamlessly repeatable loops.

These capabilities allow users to transform generated audio based on reference melodies and make detailed adjustments directly within their workflow. The user interface also assists in the process by simplified and automated creation and editing.

The automated workflow efficiency allows users to produce exactly the audio pieces they need with minimal manual intervention, streamlining the entire process.

It provides a level of control over music creation akin to what Photoshop offers for image editing. This “pixel-level control” for music enables creatives to shape, tweak, and edit their audio in highly detailed ways, providing deep control over the final output.

With its automation and fine-grained control, Project Music GenAI Control by Adobe stands out as a valuable tool in the creative industry.

5. Stable Audio 2.0 by Stability AI

Stable Audio 2.0 by Stability AI has set new standards in the field of AI music generation as the model is designed to generate high-quality audio tracks and sound effects using both text and audio inputs. It can produce full tracks with coherent musical structures up to three minutes long at 44.1kHz stereo from a single natural language prompt.

Moreover, its audio-to-audio generation capability enables users to upload audio samples and transform them using textual prompts. It enhances the flexibility and creativity of the tool. Alongside this, Stable Audio 2.0 offers amplified sound and audio effects to create diverse sounds.

Its style transfer feature allows for the seamless modification of newly generated or uploaded audio to align with a project’s specific style and tone. It enhances the customization options available to users.

 

Stable Audio 2.0 architecture
Overview of the Stable Audio 2.0 architecture – Source: stability.ai

 

Some additional aspects of the model include:

  1. Training and Dataset:
    • Stable Audio 2.0 was trained on a licensed dataset from the AudioSparx music library, which includes over 800,000 audio files containing music, sound effects, and single-instrument stems. The training process honors opt-out requests and ensures fair compensation for creators.
  2. Model Architecture:
    • Its architecture leverages a highly compressed autoencoder to condense raw audio waveforms into shorter representations. It uses a diffusion transformer (DiT) which is more adept at manipulating data over long sequences. This combination results in a model capable of recognizing and reproducing large-scale structures essential for high-quality musical compositions.
  3. Safeguards and Compliance:
    • To protect creator copyrights, Stability AI uses advanced content recognition technology (ACR) powered by Audible Magic to prevent copyright infringement. The Terms of Service require that uploads be free of copyrighted material.

Stable Audio 2.0 offers high-quality audio production, extensive sound effect generation, and flexible style transfer capabilities. It is available for free on the Stable Audio website, and it will soon be accessible via the Stable Audio API.

Hence, AI music generation has witnessed significant advancements through various models, each contributing uniquely to the field. Each of these models pushes the boundaries of what AI can achieve in music generation, offering various tools and functionalities for creators and enthusiasts alike.

While we understand the transformative impact of AI music generation models, they present their own set of limitations and challenges. It is important to understand these limitations to navigate through the available options appropriately and use these tools efficiently.

 

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Limitations and Challenges of AI Generated Music

Some prominent concerns associated with AI music generation can be categorized as follows.

Copyright Infringement

AI models like MusicLM and MusicGen often train on extensive musical datasets, which can include copyrighted material. This raises the risk of generated compositions bearing similarities to existing works, potentially infringing on copyright laws. Proper attribution and respect for original artists’ rights are vital to upholding fair practices.

Ethical Use of Training Data

The ethical use of training data is another critical issue. AI models “learn” from existing music to produce similar effects, which not all artists or users are comfortable with. This includes concerns over using artists’ work without their knowledge or consent, as highlighted by several ongoing lawsuits.

Disruption of the Music Industry

The advent of AI-generated music could disrupt the music industry, posing challenges for musicians seeking recognition in an environment flooded with AI compositions. There’s a need to balance utilizing AI as a creative tool while safeguarding the artistic individuality and livelihoods of human musicians.

 

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Bias and Originality

AI-generated music can exhibit biases or specific patterns based on the training dataset. If the dataset is biased, the generated music might also reflect these biases, limiting its originality and potentially perpetuating existing biases in music styles and genres.

Licensing and Legal Agreements

Companies like Meta claim that all music used to train their models, such as MusicGen, was covered by legal agreements with the right holders. However, the continuous evolution of licensing agreements and the legal landscape around AI-generated music remains uncertain.

 

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What is the Future of AI Music?

AI has revolutionized music creation, leading to a new level of creativity and innovation for musicians. However, it is a complex process that requires the handling of intricate details and harmonies. Plus, AI music needs to be adjustable across genre, melody, and other aspects to avoid sounding off-putting.

Today’s AI music generators, like Google’s MusicLM, are tackling these challenges. These models are designed to give creators more control over the music generation process and enhance their creative workflow.

As AI generated music continues to evolve, it’s important to use these technologies responsibly, ensuring AI serves as a tool that empowers human creativity rather than replaces it.

June 27, 2024

Integrating generative AI into edge devices is a significant challenge on its own.

You are required to smartly run advanced models efficiently within the limited computational power and memory of smartphones and computers.

Ensuring these models operate swiftly without draining battery life or overheating devices adds to the complexity.

 

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Additionally, safeguarding user privacy is crucial, requiring AI to process data locally without relying on cloud servers.

Apple has addressed these challenges with the introduction of Apple Intelligence.

This new system brings sophisticated AI directly to devices while maintaining high privacy standards.

Let’s explore the cutting-edge technology that powers Apple Intelligence and makes on-device AI possible.

Core Features of Apple Intelligence

 

Core Features of Apple Intelligence

 

  • AI-Powered Tools for Enhanced Productivity

Apple devices like iPhones, iPads, and Macs are now equipped with a range of AI-powered tools designed to boost productivity and creativity. You can use these tools to:

    • Writing and Communication: Apple’s predictive text features have evolved to understand context better and offer more accurate suggestions.This makes writing emails or messages faster and more intuitive.Moreover, the AI integrates with communication apps to suggest responses based on incoming messages, saving time and enhancing the flow of conversation.
    • Image Creation and Editing: The Photos app uses advanced machine learning to organize photos intelligently and suggest edits. For creators, features like Live Text in photos and videos use AI to detect text in images, allowing users to interact with it as if it were typed text. This can be particularly useful for quickly extracting information without manual data entry.Learn how to use AI image generation tools

       

  • Equipping Siri with Advanced AI Capabilities

Apple Intelligence has taken Siri, Apple’s virtual assistant, to the next level, making it smarter and more versatile than ever. These exciting upgrades are designed to help Siri become a more proactive and helpful assistant, seamlessly working across all your Apple devices.

    • Richer Language Understanding: Siri’s ability to understand and process natural language has been significantly enhanced. This improvement allows Siri to handle more complex queries and offer more accurate responses, mimicking a more natural conversation flow with the user.
    • On-Screen Awareness: Siri now possesses the ability to understand the context based on what is displayed on the screen.This feature allows users to make requests related to the content currently being viewed without needing to be overly specific, making interactions smoother and more intuitive.
    • Cross-App Actions: Perhaps one of the most significant updates is Siri’s enhanced capability to perform actions across multiple apps. For example, you can ask Siri to book a ride through a ride-sharing app and then send the ETA to a friend via a messaging app, all through voice commands. This level of integration across different platforms and services simplifies complex tasks, turning Siri into a powerful tool for multitasking.

 

How generative AI and LLMs work

 

Technical Innovations Behind Apple Intelligence

Apple’s strategic deployment of AI capabilities across its devices is underpinned by significant technical innovations that ensure both performance and user privacy are optimized.

These advancements are particularly evident in their dual model architecture, the application of novel post-training algorithms, and various optimization techniques that enhance efficiency and accuracy.

  • Dual Model Architecture: Balancing On-Device and Server-Based Processing

Apple employs a sophisticated approach known as dual model architecture to maximize the performance and efficiency of AI applications.

This architecture cleverly divides tasks between on-device processing and server-based resources, leveraging the strengths of each environment:

    • On-Device Processing: This is designed for tasks that require immediate response or involve sensitive data that must remain on the device.The on-device model, a ~3 billion parameter language model, is fine-tuned to efficiently execute tasks. This model excels at writing and refining text, summarizing notifications, and creating images, among other tasks, ensuring swift and responsible AI interactions
    • Server-Based Processing: More complex or less time-sensitive tasks are handled in the cloud, where Apple can use more powerful computing resources.This setup is used for tasks like Siri’s deep learning-based voice recognition, where extensive data sets can be analyzed quickly to understand and predict user queries more effectively.

The synergy between these two processing sites allows Apple to optimize performance and battery life while maintaining strong data privacy protections.

 

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  • Novel Post-Training Algorithms

Beyond the initial training phase, Apple has implemented post-training algorithms to enhance the instruction-following capabilities of its AI models.

These algorithms refine the model’s ability to understand and execute user commands more accurately, significantly improving user experience:

    • Rejection Sampling Fine-Tuning Algorithm with Teacher Committee:One of the innovative algorithms employed in the post-training phase is a rejection sampling fine-tuning algorithm,This technique leverages insights from multiple expert models (teachers) to oversee the fine-tuning of the AI.This committee of models ensures the AI adopts only the most effective behaviors and responses, enhancing its ability to follow instructions accurately and effectively.This results in a refined learning process that significantly boosts the AI’s performance by reinforcing the desired outcomes.
    • Reinforcement Learning from Human Feedback Algorithm: Another cornerstone of Apple Intelligence’s post-training improvements is the Reinforcement Learning from Human Feedback (RLHF) algorithm.This technique integrates human insights into the AI training loop, utilizing mirror descent policy optimization alongside a leave-one-out advantage estimator.Through this method, the AI learns directly from human feedback, continually adapting and refining its responses.This not only improves the accuracy of the AI but also ensures its outputs are contextually relevant and genuinely useful.The RLHF algorithm is instrumental in aligning the AI’s outputs with human preferences, making each interaction more intuitive and effective.
    • Error Correction Algorithms: These algorithms are designed to identify and learn from mistakes post-deployment.By continuously analyzing interactions, the model self-improves, offering increasingly accurate responses to user queries over time.
  • Optimization Techniques for Edge Devices

To ensure that AI models perform well on hardware-limited edge devices, Apple has developed several optimization techniques that enhance both efficiency and accuracy:

    • Low-Bit Palletization: This technique involves reducing the bit-width of the data used by the AI models. By transforming data into a low-bit format, the amount of memory required is decreased, which significantly speeds up the computation while maintaining accuracy.This is particularly important for devices with limited processing power or battery life.
    • Shared Embedding Tensors: Apple uses shared embedding tensors to reduce the duplication of similar data across different parts of the AI model.By sharing embeddings, models can operate more efficiently by reusing learned representations for similar types of data. This not only reduces the model’s memory footprint but also speeds up the processing time on edge devices.

These technical strategies are part of Apple’s broader commitment to balancing performance, efficiency, and privacy. By continually advancing these areas, Apple ensures that its devices are not only powerful and intelligent but also trusted by users for their data integrity and security.

Apple’s Smart Move with On-Device AI

Apple’s recent unveilings reveal a strategic pivot towards more sophisticated on-device AI capabilities, distinctively emphasizing user privacy.

This move is not just about enhancing product offerings but is a deliberate stride to reposition Apple in the AI landscape which has been predominantly dominated by rivals like Google and Microsoft.

  • Proprietary Technology and User-Centric Innovation: Apple’s approach centers around proprietary technologies that enhance user experience without compromising privacy.By employing dual-model architecture, Apple ensures that sensitive operations like facial recognition and personal data processing are handled entirely on-device, leveraging the power of its M-series chips.This method not only boosts performance due to reduced latency but also fortifies user trust by minimizing data exposure.
  • Strategic Partnerships and Third-Party Integrations: Apple’s strategy includes partnerships and integrations with other AI leaders like OpenAI, allowing users to access advanced AI features such as ChatGPT directly from their devices.This integration points towards a future where Apple devices could serve as hubs for powerful third-party on-device AI applications, enhancing the user experience and expanding Apple’s ecosystem.

This strategy is not just about improving what Apple devices can do; it’s also about making sure you feel safe and confident about how your data is handled.

How to Deploy On-Device AI Applications

Interested in developing on-device AI applications?

Here’s a guide to navigating the essential choices you’ll face. This includes picking the most suitable model, applying a range of optimization techniques, and using effective deployment strategies to enhance performance.

Deploying On-Device AI Applications

Read: Roadmap to Deploy On-Device AI Applications

Where Are We Headed with Apple Intelligence?

With Apple Intelligence, we’re headed towards a future where AI is more integrated into our daily lives, enhancing functionality while prioritizing user privacy.

Apple’s approach ensures that sensitive data remains on our devices, enhancing trust and performance.

By collaborating with leading AI technologies like OpenAI, Apple is poised to redefine how we interact with our devices, making them smarter and more responsive without compromising on security.

June 20, 2024

As the modern world transitions with the development of generative AI, it has also left its impact on the field of entertainment. Be it shows, movies, games, or other formats, AI has transformed every aspect of these modes of entertainment.

 

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Runway AI Film Festival is the rising aspect of this AI-powered era of media. It can be seen as a step towards recognizing the power of artificial intelligence in the world of filmmaking. One can conclude that AI is a definite part of the media industry and stakeholders must use this tool to bring innovation into their art.

 

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In this blog, we will explore the rising impact of AI films, particularly in light of the recent Runway AI Festival Film of 2024 and its role in promoting AI films. We will also navigate through the winners of this year’s festival, uncovering the power of AI in making them exceptional.

 

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Before we delve into the world of Runway AI Film Festival, let’s understand the basics of AI films.

What are AI films? What is their Impact?

AI films refer to movies that use the power of artificial intelligence in their creation process. The role of AI in films is growing with the latest advancements, assisting filmmakers in several stages of production. Its impact can be broken down into the following sections of the filmmaking process.

 

Runway AI Film Festival 2024 - AI Films
Stages of filmmaking impacted by AI

 

Pre-production and Scriptwriting

At this stage, AI is becoming a valuable asset for screenwriters. The AI-powered tools can analyze the scripts, uncover the story elements, and suggest improvements that can resonate with the audiences better. Hence, creating storylines that are more relevant and set to perform better.

 

Understand 15 Spectacular AI, ML, and Data Science Movies

Moreover, AI can even be used to generate complete drafts based on the initial ideas, enabling screenwriters to brainstorm in a more effective manner. It also results in generating basic ideas using AI that can then be refined further. Hence, AI and human writers can sync up to create strong narratives and well-developed characters.

Production and Visual Effects (VFX)

The era of film production has transitioned greatly, owing to the introduction of AI tools. The most prominent impact is seen in the realm of visual effects (VFX) where AI is used to create realistic environments and characters. It enables filmmakers to breathe life into their imaginary worlds.

 

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Hence, they can create outstanding creatures and extraordinary worlds. The power of AI also results in the transformation of animation, automating processes to save time and resources. Even de-aging actors is now possible with AI, allowing filmmakers to showcase a character’s younger self.

Post-production and Editing

While pre-production and production processes are impacted by AI, its impact has also trickled into the post-production phase. It plays a useful role in editing by tackling repetitive tasks like finding key scenes or suggesting cuts for better pacing. It gives editors more time for creative decisions.

 

Know how  AI as a Service (AIaaS) transforms the Industry

AI is even used to generate music based on film elements, giving composers creative ideas to work with. Hence, they can partner up with AI-powered tools to create unique soundtracks that form a desired emotional connection with the audience.

AI-Powered Characters

With the rising impact of AI, filmmakers are using this tool to even generate virtual characters through CGI. Others who have not yet taken such drastic steps use AI to enhance live-action performances. Hence, the impact of AI remains within the characters, enabling them to convey complex emotions more efficiently.

Thus, it would not be wrong to say that AI is revolutionizing filmmaking, making it both faster and more creative. It automates tasks and streamlines workflows, leaving more room for creative thinking and strategy development. Plus, the use of AI tools is revamping filmmaking techniques, and creating outstanding visuals and storylines.

 

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With the advent of AI in the media industry, the era of filmmaking is bound to grow and transition in the best ways possible. It opens up avenues that promise creativity and innovation in the field, leading to amazing results.

 

How generative AI and LLMs work

 

Why Should We Watch AI Films?

In this continuously changing world, the power of AI is undeniable. While we welcome these tools in other aspects of our lives, we must also enjoy their impact in the world of entertainment. These movies push the boundaries of visual effects, crafting hyper-realistic environments and creatures that wouldn’t be possible otherwise.

Hence, giving life to human imagination in the most accurate way. It can be said that AI opens a portal into the human mind that can be depicted in creative ways through AI films. This provides you a chance to navigate alien landscapes and encounter unbelievable characters simply through a screen.

 

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However, AI movies are not just about the awe-inspiring visuals and cinematic effects. Many AI films delve into thought-provoking themes about artificial intelligence, prompting you to question the nature of consciousness and humanity’s place in a technology-driven world.

Such films initiate conversations about the future and the impact of AI on our lives. Thus, AI films come with a complete package. From breathtaking visuals and impressive storylines to philosophical ponderings, it brings it all to the table for your enjoyment. Take a dive into AI films, you might just be a movie away from your new favorite genre.

To kickstart your exploration of AI films, let’s look through the recent film festival about AI-powered movies.

 

What is the Runway AI Film Festival?

It is an initiative taken by Runway, a company that works to develop AI tools and bring AI research to life in their products. Found in 2018, the company has been striving for creativity with its research in AI and ML through in-house work and collaborating globally.

In an attempt to recognize and celebrate the power of AI tools, they have introduced a global event known as the Runway AI Film Festival. It aims to showcase the potential of AI in filmmaking. Since the democratization of AI tools for creative personnel is Runway’s goal, the festival is a step towards achieving it.

The first edition of the AI film festival was put forward in 2023. It became the initiation point to celebrate the collaboration of AI and artists to generate mind-blowing art in the form of films. The festival became a platform to recognize and promote the power of AI films in the modern-day entertainment industry.

Details of the AI Film Festival (AIFF)

The festival format allows participants to submit their short films for a specified period of time. Some key requirements that you must fulfill include:

  • Your film must be 1 to 10 minutes long
  • An AI-powered tool must be used in the creation process of your film, including but not limited to generative AI
  • You must submit your film via a Runway AI company link

While this provides a glimpse of the basic criteria for submissions at a Runway AI Film Festival, they have provided detailed submission guidelines as well. You must adhere to these guidelines when submitting your film to the festival.

These submissions are then judged by a panel of jurors who score each submission. The scoring criteria for every film is defined as follows:

  • The quality of your film composition
  • The quality and cohesion of your artistic message and film narrative
  • The originality of your idea and subsequently the film
  • Your creativity in incorporating AI techniques

Each juror scores a submission from 1-10 for every defined criterion. Hence, each submission gets a total score out of 40. Based on this scoring, the top 10 finalists are announced who receive cash prizes and Runway credits. Moreover, they also get to screen their films at the gala screenings in New York and Los Angeles.

 

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Runway AI Film Festival 2024

The Film Festival of 2024 is only the second edition of this series and has already gained popularity in the entertainment industry and its fans. While following the same format, this series of festivals is becoming a testament to the impact of AI in filmmaking and its boundless creativity.

 

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So far, we have navigated through the details of AI films and the Runway AI Film Festival, so it is only fair to navigate through the winners of the 2024 edition.

Winners of the 2024 Festival

1. Get Me Out / 囚われて by Daniel Antebi

Runtime: 6 minutes 34 seconds

Revolving around Aka and his past, it navigates through his experiences while he tries to get out of a bizarre house in the suburbs of America. Here, escape is an illusion, and the house itself becomes a twisted mirror, forcing Aka to confront the chilling reflections of his past.

Intrigued enough? You can watch it right here.

 

 

2. Pounamu by Samuel Schrag

Runtime: 4 minutes 48 seconds

It is the story of a kiwi bird as it chases his dream through the wilderness. As it pursues a dream deeper into the heart of the wild, it might hold him back but his spirit keeps him soaring.

 

 

3. e^(i*π) + 1 = 0 by Junie Lau

Runtime: 5 minutes 7 seconds

A retired mathematician creates digital comics, igniting an infinite universe where his virtual children seek to decode the ‘truth,’. Armed with logic and reason, they journey across time and space, seeking to solve the profound equations that hold the key to existence itself.

 

 

4. Where Do Grandmas Go When They Get Lost? by Léo Cannone

Runtime: 2 minutes 27 seconds

Told through a child’s perspective, the film explores the universal question of loss and grief after the passing of a beloved grandmother. The narrative is a delicate blend of whimsical imagery and emotional depth.

 

 

5. L’éveil à la création / The dawn of creation by Carlo De Togni & Elena Sparacino

Runtime: 7 minutes 32 seconds

Gauguin’s journey to Tahiti becomes a mystical odyssey. On this voyage of self-discovery, he has a profound encounter with an enigmatic, ancient deity. This introspective meeting forever alters his artistic perspective.

 

 

6. Animitas by Emeric Leprince

Runtime: 4 minutes

A tragic car accident leaves a young Argentine man trapped in limbo.

 

 

7. A Tree Once Grew Here by John Semerad & Dara Semerad

Runtime: 7 minutes

Through a mesmerizing blend of animation, imagery, and captivating visuals, it delivers a powerful message that transcends language. It’s a wake-up call, urging us to rebalance our relationship with nature before it’s too late.

 

 

8. Dear Mom by Johans Saldana Guadalupe & Katie Luo

Runtime: 3 minutes 4 seconds

It is a poignant cinematic letter written by a daughter to her mother as she explores the idea of meeting her mother at their shared age of 20. It’s a testament to unconditional love and gratitude.

 

 

9. LAPSE by YZA Voku

Runtime: 1 minute 47 seconds

Time keeps turning, yet you never quite find your station on the dial. You drift between experiences, a stranger in each, the melody of your life forever searching for a place to belong.

 

 

10. Separation by Rufus Dye-Montefiore, Luke Dye-Montefiore & Alice Boyd

Runtime: 4 minutes 52 seconds

It is a thought-provoking film that utilizes a mind-bending trip through geologic time. As the narrative unfolds, the film ponders a profound truth: both living beings and the world itself must continually adapt to survive in a constantly evolving environment.

 

 

How will AI Film Festivals Impact the Future of AI Films?

Events like the Runway AI Film Festival are shaping the exciting future of AI cinema. These festivals highlight the innovation of films, generating buzz and attracting new audiences and creators. Hence, growing the community of AI filmmakers.

 

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These festivals like AIFF offer a platform that fosters collaboration and knowledge sharing, boosting advancements in AI filmmaking techniques. Moreover, they will help define the genre of AI films with a bolder use of AI in storytelling and visuals. It is evident that AI film festivals will play a crucial role in the advanced use of AI in filmmaking.

May 29, 2024

In the recent discussion and advancements surrounding artificial intelligence, there’s a notable dialogue between discriminative and generative AI approaches. These methodologies represent distinct paradigms in AI, each with unique capabilities and applications.

Yet the crucial question arises: Which of these emerges as the foremost driving force in AI innovation? In this blog, we will explore the details of both approaches and navigate through their differences. We will also revisit some real-world applications of both approaches.

What is Generative AI?

 

discriminative vs generative AI - what is generative AI
Source: Medium

 

Generative AI is a growing area in machine learning, involving algorithms that create new content on their own. These algorithms use existing data like text, images, and audio to generate content that looks like it comes from the real world.

This approach involves techniques where the machine learns from massive amounts of data. The process involves understanding how the data is structured, recognizing design patterns, and underlying relationships within the data.

Once the model is trained on the available data, it can generate new content based on the learned patterns. This approach promotes creativity and innovation in the content-generation process. Generative AI has extensive potential for growth and the generation of new ideas.

 

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Generative models that enable this AI approach to perform enable an in-depth understanding of the data they use to train. Some common generative models used within the realm of generative AI include:

  • Bayesian Network – it allows for probabilistic reasoning over interconnected variables to calculate outcomes in various situations
  • Autoregressive Models – they predict the next element in a sequence (like text or images) one by one, building on previous elements to create realistic continuations
  • Generative Adversarial Network (GAN) – uses a deep learning approach with two models: a generator that creates new data and a discriminator that tests if the data is real or AI-generated

What is Discriminative AI?

 

discriminative vs generative AI - what is discriminative AI
A visual representation of discriminative AI – Source: Medium

 

Discriminative modeling, often linked with supervised learning, works on categorizing existing data. By spotting features in the data, discriminative models help classify the input into specific groups without looking deep into how the data is spread out.

 

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Models that manage discriminative AI are also called conditional models. Some common models used are as follows:

  • Logistic Regression – it classifies by predicting the probability of a data point belonging to a class instead of a continuous value
  • Decision Trees – uses a tree structure to make predictions by following a series of branching decisions
  • Support Vector Machines (SVMs) – create a clear decision boundary in high dimensions to separate data classes
  • K-Nearest Neighbors (KNNs) – classifies data points by who their closest neighbors are in the feature space

 

Generative AI vs Discriminative AI: Understanding the 5 Key Differences | Data Science Dojo

 

Generative vs Discriminative AI: A Comparative Insight

While we have explored the basics of discriminative and generative AI, let’s look deeper into the approaches through a comparative lens. It is clear that both approaches process data in a different manner, resulting in varying outputs. Hence, each method has its own strengths and uses.

 

Comparing generative and discriminative AI
Generative vs discriminative AI

 

Generative AI is great for sparking creativity and new ideas, leading to progress in art, design, and finding new drugs. By understanding how data is set up, generative models can help make new discoveries possible. 

 

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On the other hand, discriminative AI is all about being accurate and fast, especially in sorting things into groups in various fields. Its knack for recognizing patterns comes in handy for practical ideas. 

Generative AI often operates in unsupervised or semi-supervised learning settings, generating new data points based on patterns learned from existing data. This capability makes it well-suited for scenarios where labeled data is scarce or unavailable.

 

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In contrast, discriminative AI primarily operates in supervised learning settings, leveraging labeled data to classify input into predefined categories. While this approach requires labeled data for training, it often yields superior performance in classification tasks due to its focus on learning discriminative features.

 

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Hence, generative AI encourages exploration and creativity through the generation of new content and discriminative AI prioritizes practicality and accuracy in classification tasks.

Together, these complementary approaches form a symbiotic relationship that drives AI progress, opening new avenues for innovation and pushing the boundaries of technological advancement.

Real-World Applications of Generative and Discriminative AI

Let’s discuss the significant contributions of both generative and discriminative AI in driving innovation and solving complex problems across various domains.

Use Cases of Generative AI

A notable example is DeepMind’s AlphaFold, an AI system designed to predict protein folding, a crucial task in understanding the structure and function of proteins.

 

 

Released in 2020, AlphaFold leverages deep learning algorithms to accurately predict the 3D structure of proteins from their amino acid sequences, outperforming traditional methods by a significant margin. This breakthrough has profound implications for drug development, as understanding protein structures can aid in designing more effective therapeutics.

AlphaFold’s success in the recent Critical Assessment of Structure Prediction (CASP) competition, where it outperformed other methods, highlights the potential of generative AI in advancing scientific research and accelerating drug discovery processes.

Other use cases of generative AI include:

  • Netflix – for personalized recommendations to boost user engagement and satisfaction
  • Grammarly – for identifying errors, suggesting stylistic improvements, and analyzing overall effectiveness
  • Adobe Creative Cloud – for concept generation, prototyping tools, and design refinement suggestions

 

How generative AI and LLMs work

 

Use Cases of Discriminative AI 

Discriminative AI has found widespread application in natural language processing (NLP) and conversational AI. A prominent example is Google’s Duplex, a technology that enables AI assistants to make phone calls on behalf of users for tasks like scheduling appointments and reservations.

Duplex leverages sophisticated machine learning algorithms to understand natural language, navigate complex conversations, and perform tasks autonomously, mimicking human-like interactions seamlessly. Released in 2018, Duplex garnered attention for its ability to handle real-world scenarios, such as making restaurant reservations, with remarkable accuracy and naturalness.

Its discriminative AI capabilities allow it to analyze audio inputs, extract relevant information, and generate appropriate responses, showcasing the power of AI-driven conversational systems in enhancing user experiences and streamlining business operations.

Additional use cases of discriminative AI can be listed as:

  • Amazon – analyzes customer behavior to recommend products of interest, boosting sales and satisfaction
  • Facebook – combats spam and hate speech by identifying and removing harmful content from user feeds
  • Tesla Autopilot – navigates roads, allowing its cars to identify objects and make driving decisions

 

 

Which is the Right Approach?

Discriminative and generative AI take opposite approaches to tackling classification problems. Generative models delve into the underlying structure of the data, learning its patterns and relationships. In contrast, discriminative models directly target the decision boundary, optimizing it for the best possible classification accuracy.

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Understanding these strengths is crucial for choosing the right tool for the job. By leveraging the power of both discriminative and generative models, we can build more accurate and versatile machine-learning solutions, ultimately shaping the way we interact with technology and the world around us.

May 27, 2024

Generative AI represents a significant leap forward in the field of artificial intelligence. Unlike traditional AI, which is programmed to respond to specific inputs with predetermined outputs, generative AI can create new content that is indistinguishable from that produced by humans.

It utilizes machine learning models trained on vast amounts of data to generate a diverse array of outputs, ranging from text to images and beyond. However, as the impact of AI has advanced, so has the need to handle it responsibly.

 

Explore Top 7 Generative AI courses offered online

In this blog, we will explore how AI can be handled responsibly, producing outputs within the ethical and legal standards set in place. Hence answering the question of ‘What is responsible AI?’ in detail.

 

What is Responsible AI? 5 Core Responsible AI Principles | Data Science Dojo

 

However, before we explore the main principles of responsible AI, let’s understand the concept.

What is Responsible AI?

Responsible AI is a multifaceted approach to the development, deployment, and use of Artificial Intelligence (AI) systems. It ensures that our interaction with AI remains within ethical and legal standards while remaining transparent and aligning with societal values.

Responsible AI refers to all principles and practices that aim to ensure AI systems are fair, understandable, secure, and robust. The principles of responsible AI also allow the use of generative AI within our society to be governed effectively at all levels.

 

Explore some key ethical issues in AI that you must know

 

The Importance of Responsibility in AI Development

With great power comes great responsibility, a sentiment that holds particularly true in the realm of AI development. As generative AI technologies grow more sophisticated, they also raise ethical concerns and the potential to significantly impact society.

 

Understand the Generative AI roadmap

 

It’s crucial for those involved in AI creation — from data scientists to developers — to adopt a responsible approach that carefully evaluates and mitigates any associated risks. To dive deeper into Generative AI’s impact on society and its ethical, social, and legal implications, tune in to our podcast now!

 

 

Core Principles of Responsible AI

 

Core Principles of Responsible AI: what is Responsible AI

 

Let’s delve into the core responsible AI principles:

Fairness

This principle is concerned with how an AI system impacts different groups of users, such as by gender, ethnicity, or other demographics. The goal is to ensure that AI systems do not create or reinforce unfair biases and that they treat all user groups equitably. 

 

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Privacy and Security

AI systems must protect sensitive data from unauthorized access, theft, and exposure. Ensuring privacy and security is essential to maintain user trust and to comply with legal and ethical standards concerning data protection.

 

How generative AI and LLMs work

 

Explainability

This entails implementing mechanisms to understand and evaluate the outputs of an AI system. It’s about making the decision-making process of AI models transparent and understandable to humans, which is crucial for trust and accountability, especially in high-stakes scenarios for instance in finance, legal, and healthcare industries.

Transparency

This principle is about communicating information about an AI system so that stakeholders can make informed choices about their use of the system. Transparency involves disclosing how the AI system works, the data it uses, and its limitations, which is fundamental for gaining user trust and consent. 

Governance

It refers to the processes within an organization to define, implement, and enforce responsible AI practices. This includes establishing clear policies, procedures, and accountability mechanisms to govern the development and use of AI systems.

 

what is responsible AI? The core pillars
Source: Analytics Vidhya

 

These principles are integral to the development and deployment of AI systems that are ethical, fair, and respectful of user rights and societal norms.

How to build Responsible AI?

Here’s a step-by-step guide to building trustworthy AI systems.

Identify potential harms

This step is about recognizing and understanding the various risks and negative impacts that generative AI applications could potentially cause. It’s a proactive measure to consider what could go wrong and how these risks could affect users and society at large.

This includes issues of privacy invasion, amplification of biases, unfair treatment of certain user groups, and other ethical concerns. 

Measure the presence of these harms

Once potential harms have been identified, the next step is to measure and evaluate how and to what extent these issues are manifested in the AI system’s outputs.

This involves rigorous testing and analysis to detect any harmful patterns or outcomes produced by the AI. It is an essential process to quantify the identified risks and understand their severity.

 

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Mitigate the harms

After measuring the presence of potential harms, it’s crucial to actively work on strategies and solutions to reduce their impact and presence. This might involve adjusting the training data, reconfiguring the AI model, implementing additional filters, or any other measures that can help minimize the negative outcomes.

Moreover, clear communication with users about the risks and the steps taken to mitigate them is an important aspect of this component, ensuring transparency and maintaining trust. 

Operate the solution responsibly

The final component emphasizes the need to operate and maintain the AI solution in a responsible manner. This includes having a well-defined plan for deployment that considers all aspects of responsible usage.

 

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It also involves ongoing monitoring, maintenance, and updates to the AI system to ensure it continues to operate within the ethical guidelines laid out. This step is about the continuous responsibility of managing the AI solution throughout its lifecycle.

 

Responsible AI reference architecture
Source: Medium

 

Let’s take a practical example to further understand how we can build trustworthy and responsible AI models. 

Case study: Building a responsible AI chatbot

Designing AI chatbots requires careful thought not only about their functional capabilities but also their interaction style and the underlying ethical implications. When deciding on the personality of the AI, we must consider whether we want an AI that always agrees or one that challenges users to encourage deeper thinking or problem-solving.

How do we balance representing diverse perspectives without reinforcing biases?

The balance between representing diverse perspectives and avoiding the reinforcement of biases is a critical consideration. AI chatbots are often trained on historical data, which can reflect societal biases.

 

Here’s a guide on LLM chatbots, explaining all you need to know

 

For instance, if you ask an AI to generate an image of a doctor or a nurse, the resulting images may reflect gender or racial stereotypes due to biases in the training data. 

However, the chatbot should not be overly intrusive and should serve more as an assistive or embedded feature rather than the central focus of the product. It’s important to create an AI that is non-intrusive and supports the user contextually, based on the situation, rather than dominating the interaction.

 

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The design process should also involve thinking critically about when and how AI should maintain a high level of integrity, acknowledging the limitations of AI without consciousness or general intelligence. AI needs to be designed to sound confident but not to the extent that it provides false or misleading answers. 

Additionally, the design of AI chatbots should allow users to experience natural and meaningful interactions. This can include allowing the users to choose the personality of the AI, which can make the interaction more relatable and engaging. 

By following these steps, developers and organizations can strive to build AI systems that are ethical, fair, and trustworthy, thus fostering greater acceptance and more responsible utilization of AI technology. 

Interested in learning how to implement AI guardrails in RAG-based solutions? Tune in to our podcast with the CEO of LlamaIndex now.

 

May 21, 2024

Generative AI has reshaped the digital landscape with smarter tools working more efficiently than ever before. AI-powered tools have impacted various industries like finance, healthcare, marketing, and others. While it has transitioned all areas, the field of engineering is not unaffected.

 

Explore the Top 7 Generative AI Courses offered online   

The engineering world has experienced a new boost with the creation of the first-ever AI software engineer, thanks to Cognition AI. The company has launched its addition to the realm of generative AI with the name of Devin AI.

A software engineer focuses on software development. It refers to the process of