Part 1:

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks (ANN)
• Deep Learning
• Generative AI (GAI)
• Foundation Models
• Large Language Models
• Natural Language Processing (NLP)
• Transformer Model
• Generative Pretrained Transformer (GPT)

Part 2:

• Responsible AI
• Labelled data
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Prompt engineering
• Prompt chaining
• Retrieval augmented generation (RAG)
• Parameters
• Fine Tuning

Bias

When it comes to machine learning, Bias is considered to be an issue in which elements of the data set being used to train the model have weighted distortion of statistical data.  This may unfairly and inaccurately sway the measurement and analysis of the training data, and therefore will produce biassed and prejudiced results.  This makes it essential to have high quality data when training models, as data that is incomplete and of low quality can produce unexpected and unreliable algorithm results due to inaccurate assumptions.

Hallucination

AI hallucinations occur when an AI program falsy generates responses that are made to appear factual and true.  Although hallucinations can be a rare occurrence, this is one good reason as to why you shouldn’t take all responses as granted.  Causes of hallucinations could be create through the adoption of biassed data, or simply generated using unjustified responses through the misinterpretation of data when training.  The term hallucination is used as it’s similar to the way humans can hallucinate by experiencing something that isn’t real.       

Temperature

When it comes to AI, temperature is a parameter that allows you to adjust how random the response output from your models will be.  Depending on how the temperature is set will determine how focused or convoluted the output that is generated will be.  The temperature range is typically between 0 and 1, with a default value of 0.7.  When it’s set closer to 0, the more concentrated the response, as the number gets higher, then the more diverse it will be.

Anthropomorphism

Anthropomorphism is that way in which the assignment of the human form, such as emotions, behaviours and characteristics are attributed to non-human ‘things’, including machines, animals, inanimate objects, the environment and more.  Through the use of AI, and as it develops further and becomes more complex and powerful, people can begin to anthropomorphize with computer programmes, even after very short exposures to it, which can influence people’s behaviours interacting with it.  

Completion

The term completion is used specifically within the realms of NLP models to describe the output that is generated from a response.  For example, if you were using ChatGTP, and you asked it a question, the response generated and returned to you as the user would be considered the ‘completion’ of that interaction.

Tokens

A token can be seen as words and text supplied as an input to a prompt, it can be a whole word, just the beginning or the word, the end, spaces, single characters and anything in between, depending on the tokenization method being used.  These tokens are classed as small basic units used by LLMs to process and analyse input requests allowing it to generate a response based upon the tokens and patterns detected.  Different LLMs will have different token capacities for both the input and output of data which is defined as the context window.   

Emergence in AI

Emergence in AI will typically happen when a model scales in such size with an increasing number of parameters being used that it leads to unexpected behaviours that would not be possible to identify within a smaller model.  It develops an ability to learn and adjust without being specifically trained to do so in that way.  Risks and complications can arise in emergence behaviour in AI, for example, the system could develop its own response to a specific event which could lead to damaging and harmful consequences which it has not been explicitly trained to do.

Embeddings

AI embeddings are numerical representations of objects, words, or entities in a multi-dimensional space. Generated through machine learning algorithms, embeddings capture semantic relationships and similarities. In natural language processing, word embeddings convert words into vectors, enabling algorithms to understand context and meaning. Similarly, in image processing, embeddings represent images as vectors for analysis. These compact representations enhance computational efficiency, enabling AI systems to perform tasks such as language understanding, image recognition, and recommendation more effectively.

Text Classification

Text classification involves training a model to categorise and assign predefined labels to input text based on its content. Using techniques like natural language processing, the system learns patterns and context to analyse the structure from the input text and make accurate predictions on its sentiment, topic categorization and intent. AI text classifiers generally possess a wide understanding of different languages and contexts, which enables them to handle various tasks across different domains with adaptability and efficiency.

Context Window

The context window refers to how much text or information that an AI model can process and respond with through prompts.  This closely relates to the number of tokens that are used within the model, and this number will vary depending on which model you are using, and so will ultimately determine the size of the context window. Prompt engineering plays an important role when working within the confines of a specific content window.

That now brings me to the end of this blog series and so I hope you now have a greater understanding of some of the common vocabulary used when discussing generative AI, and artificial intelligence.

The post Navigating the Vocabulary of Generative AI Series (3 of 3) appeared first on Cloud Academy.

• Artificial Intelligence
• Machine Learning
• Artificial Neural Networks (ANN)
• Deep Learning
• Generative AI (GAI)
• Foundation Models
• Large Language Models
• Natural Language Processing (NLP)
• Transformer Model
• Generative Pretrained Transformer (GPT)

Responsible AI

Responsible AI is designed to set out the principles and practices when working with artificial intelligence to ensure that it is adopted, implemented and executed fairly, lawfully, ethically ensuring trust and transparency is given to the business and its customers.  Considerations to how AI is used and how it may affect humanity must be governed and controlled by rules and frameworks.  Trust, assurance, faith and confidence should be embedded with any models and applications that are built upon AI. 

Labelled Data

Labelled data is used to help machine learning models and algorithms process and learn from raw material.  The data is ‘labelled’ as it contains tags and features associated with the target data which provides useful and informative information about it, for example if you had a photo of a tiger, it could be labelled with ‘Tiger’. This helps to provide context to the raw data which the ML model can then use and extract to help it to learn and recognise other images of tigers.  This raw input data can be in the form of text, images, videos and more and requires human intervention to label the data correctly.

Supervised learning

Supervised learning is a training method used within machine learning which uses a vast amount of labelled datasets in order to be able to predict output variables.  Over time, the algorithms learn how to define the relationship between the labelled input data and the predicted output data using mapping functions.  As it learns, the algorithm is corrected if it makes an incorrect output mapping from the input data, and therefore the learning process is considered to be ‘supervised’.  For example, if it saw a photo of a lion and classified it as a tiger, the algorithm would be corrected and the data sent back to retrain.

Unsupervised learning

Unsupervised learning differs from supervised learning in that supervised learning uses labelled data, and unsupervised learning does not.  Instead it is given full autonomy in identifying characteristics about the unlabeled data and differences, structure and relationships between each data point.  For example, if the unlabeled data contained images of tigers, elephants and giraffes, the machine learning model would need to establish and classify specific features and attributes from each picture to determine the difference between the images, such as colour, patterns, facial features, size and shape.

Semi-supervised learning

This is a method of learning that uses a combination of both supervised and unsupervised learning techniques and so uses both labelled and unlabeled data in its process.  Typically when using this method, you have a smaller data set of labelled data compared to a larger data set of unlabelled data, this prevents you having to tag a huge amount of data.  As a result this enables you to use the smaller set of supervised learning to assist in the training of the model and so aids in the classification of data points using the unsupervised learning technique.  

Prompt Engineering

Prompt engineering allows you to facilitate the refinement of input prompts when working with large language models to generate the most appropriate outputs.  The technique of prompt engineering enables you to enhance the performance of your generative AI models to carry out specific tasks by optimising prompts.  By making adjustments and alterations to input prompts you can manipulate the output and behaviour of the AI responses making them more relevant. Prompt engineering is a principle that is allowing us to transform how humans are interacting with AI.

Prompt Chaining

Prompt chaining is a technique used when working with large language models and NLP, which allows for conversational interactions to occur based on previous responses and inputs.  This creates a contextual awareness through a succession of continuous prompts creating a human-like exchange of language and interaction.  As a result, this is often successfully implemented with chat-bots.  This enhances the user’s experience by responding to bite-sized blocks of data (multiple prompts) instead of working with a single and comprehensive prompt which could be difficult to respond to.

Retrieval augmented generation (RAG)

RAG is a framework used within AI that enables you to supply additional factual data to a foundation model as an external source to help it generate responses using up-to-date information.  A foundation model is only as good as the data that it has been trained on, and so if there are irregularities in your responses, you can supplement the model with additional external data which allows the model to have the most recent, reliable and accurate data to work with.  For example, if you asked ‘what’s the latest stock information for Amazon’ RAG would take that question and discover this information using external sources, before generating the response. This up-to-date information would not be stored within the associated foundation model being used

Parameters

AI parameters are the variables within a machine learning model that the algorithm adjusts during training to enable it to optimise its performance to generalise the patterns from data, and therefore making them more efficient. These values dictate the model’s behaviour and minimise the difference between predicted and actual outcomes.

Fine Tuning

Fine-tuning is the technique of adjusting a pre-trained model on a particular task or data set to improve and enhance its performance.  Initially trained on a broad data set, the model can be fine-tuned using a smaller, and more task-specific data set. This technique allows the model to alter and adapt its parameters to better suit the nuances of the new data, improving its accuracy and effectiveness for the targeted application.

In my next post I continue to focus on AI, and I will be talking about the following topics:

• Bias
• Hallucinations
• Temperature
• Anthropomorphism
• Completion
• Tokens
• Emergence in AI
• Embeddings
• Text Classification
• Context Window

The post Navigating the Vocabulary of Generative AI Series (2 of 3) appeared first on Cloud Academy.

Gen AI is already rapidly changing our daily lives and will continue to do so as the technology is being adopted at an exponential rate. Those within the tech industry need to be aware of the fundamentals and understand how it fits together, and to do this you need to know what a few components are. You can easily become lost in a conversation if you are unaware of what a foundation model (FM), large language model (LLM), or what prompt engineering is and why it’s important.  

In this blog series, I want to start by taking it back to some of the fundamental components of artificial intelligence (AI) and looking at the subset of technologies that have been derived from AI and then dive deeper as we go.

If you want to deep dive into AI, Cloud Academy has a whole dedicated section in its training library. Also, if you’re looking to channel the power of AI in your business, request a free demo today!

Artificial intelligence (AI)

AI can be defined as the simulation of our own human intelligence that is managed and processed by computer systems.  AI can be embedded as code within a small application on your phone, or perhaps at the other end of the scale, implemented within a large-scale enterprise application hosted within the cloud and accessed by millions of customers.  Either way, it has the capabilities to complete tasks and activities that may have previously required human intelligence to complete.  

Machine Learning (ML)

Machine learning is a subset of AI, and is used as a means to enable computer-based systems to be taught based upon experience and data using mathematical algorithms.  Over time, performance is improved and accuracy is increased as it learns from additional sampled data enabling patterns to be established and predictions to be made.  This creates an-going cycle which enables ML to learn, grow, evolve and remodel without human invention.

Artificial Neural Network (ANN)

Neural networks are a subset of Machine Learning that are used to instruct and train computers to learn how to develop and recognize patterns using a network designed not dis-similar to that of the human brain. Using a network consisting of complex and convoluted layered and interconnected artificial nodes and neurons, it is capable of responding to different input data to generate the best possible results, learning from mistakes to enhance its accuracy in delivering results.  

Deep Learning (DL)

Deep learning uses artificial neural networks to detect, identify, and classify data by analysing patterns, and is commonly used across sound, text, and image files.  For example, it can identify and describe objects within a picture, or it can transcribe an audio file into a text file.  Using multiple layers of the neural network, it can dive ‘deep’ to highlight complex patterns using supervised, unsupervised, or semi-supervised learning models

Generative AI (GAI)

Generative AI, or Gen AI is a subset of deep learning and refers to models that are capable of producing new and original content that has never been created before, this could be an image, some text, new audio, code, video and more.  The creation of this content is generated using huge amounts of training data within foundation models, and as a result it creates output that is similar to this existing data, which could be mistaken to have been created by humans.

Foundation Model (FM)

Foundation models are trained on monumental unlabeled broad data sets and underpin the capabilities of Gen AI, this makes them considerably bigger than traditional ML models which are generally used for more specific functions.  FMs are used as the baseline starting point for developing and creating models which can be used to interpret and understand language, converse in conversational messaging, and also create and generate images.  Different foundation models can specialise in different areas, for example the Stable Diffusion model by Stability AI is great for image generation, and the GPT-4 model is used by ChatGPT for natural language.  FMs are able to produce a range of outputs based on prompts with high levels of accuracy.  

Large Language Model (LLM)  

Large language models are used by generative AI to generate text based on a series of probabilities, enabling them to predict, identify and translate consent.  Trained on transformer models using billions of parameters, they focus on patterns and algorithms that are used to distinguish and simulate how humans use language through natural language processing (NLP).  LLMs are often used to summarise large blocks of text, or in text classification to determine its sentiment, and to create chatbots and AI assistants.

Natural Language Processing (NLP)

NLP is a discipline that focuses on linguistics and provides the capacity for computer based systems to understand and interpret how language is used in both written and verbal forms, as if a human was writing or speaking it.  Natural language understanding (NLU), looks at the understanding of the sentiment, intent, and meaning in language, whilst natural language generation (NLG) focuses on the generation of language, both written and verbal, allowing text-to-speech and speech-to-text output.

Transformer Model

A transformer model is used within deep learning architecture and can be found supporting the root of many large language models due to its ability to process text using mathematical techniques in addition to capturing the relationships between the text. This long-term memory allows the model to transfer text from one language to another. It can also identify relationships between different mediums of data, allowing applications to ‘transform’ text (input), into an image (output).  

Generative Pretrained Transformer (GPT)

Generative pre-trained transformers use the Transformer model based upon deep learning to create human-like capabilities to generate content primarily using text, images, and audio using natural language processing techniques.  Used extensively in Gen AI use cases such as text summarization, chatbots, and more.  You will likely have heard of ChatGPT, which is a based on a generative pretrained transformer model.

In my next post I continue to focus on AI, and I will be talking about the following topics:

• Responsible AI
• Labelled Data
• Supervised learning
• Unsupervised learning
• Semi-supervised learning
• Prompt engineering
• Prompt chaining
• Retrieval Augmented Generation (RAG)
• Parameters
• Fine Tuning

The post Navigating the Vocabulary of Generative AI Series (1 of 3) appeared first on Cloud Academy.

What makes this AI model different, is that it was built from the ground up to be multimodal.  That means Gemini can understand and process information from various sources, including text, images, audio, video, and code.  It can also transform any type of input into any type of output.  This sets it apart from earlier models that were limited to handling specific types of data.

Capabilities

As a result, Gemini can:

• Generate text and images: This should result in more engaging and interactive experiences, and even open the doors to new forms of artistic expression.
• Answer complex questions: With its multimodal understanding, Gemini is able to tackle intricate queries that span multiple domains.
• Explain complex concepts: Through its sophisticated reasoning abilities, Gemini can break down complicated ideas into easily digestible explanations.
• Write code: Gemini can understand and generate code in multiple languages, making it a valuable tool for programmers.
• Surpass human experts: On the MMLU benchmark, Gemini outperformed human experts, demonstrating its superior knowledge and problem-solving skills in over 50 different domains.

Applications

If all this is true, the applications could be almost endless.

• Science: By analyzing vast amounts of data, Gemini could accelerate scientific discoveries and breakthroughs.
• Education: With Gemini’s ability to understand diverse information, personalized learning experiences could be tailor-built to match individual needs.
• Healthcare: Gemini could assist with medical diagnosis and treatment by analyzing complex data and making custom recommendations.
• Arts: Gemini could empower artists and creators to explore new forms of expression and push the boundaries of creativity.

Versions

Gemini will be available in three sizes:

• Gemini Ultra: The largest and most powerful model for highly complex tasks.
• Gemini Pro: Best performing model for a wide range of tasks.
• Gemini Nano: The most efficient model for use on mobile devices.

Availability

Starting December 2023, Gemini will be integrated into various Google products and services including:

• Bard: Google’s AI chatbot is already utilizing Gemini Pro for advanced reasoning and understanding.  Gemini Ultra will be added to Bard early next year to create a new experience called Bard Advanced.
• Pixel: Pixel 8 Pro will be the first smartphone to run Gemini Nano, powering new features like Summarize in the Recorder app.
• Search: Gemini will be used to provide more relevant and informative search results.
• Ads: Gemini will optimize ad targeting for greater effectiveness.
• Chrome: Gemini will enhance the browsing experience with personalized features.
• Duet AI: Gemini will power Duet AI for more seamless and natural interactions.

The Future of AI

With its exceptional capabilities, Gemini could be a significant leap forward in AI development.  It just might have the potential to transform the way we live, work, and interact with the world.

Additional resources

• Read the official announcement: https://blog.google/technology/ai/google-gemini-ai/
• Try the new Bard chatbot powered by Gemini Pro: https://bard.google.com/

The post Google Unveils Gemini AI appeared first on Cloud Academy.

If you want to deep dive into AI, Cloud Academy has a whole dedicated section in its training library. Also, if you’re looking to channel the power of AI in your business, request a free demo today!

The History of AI

Understanding the history of AI provides a broader context for generative AI.

The roots of AI can be traced back to early philosophers and mathematicians who aimed to mechanize reasoning. However, the groundwork for modern AI was established in the 19th and 20th centuries, epitomized by George Boole’s Boolean algebra and Alan Turing’s concept of thinking machines.

In 1943, Warren McCullouch and Walter Pitts introduced the first artificial neuron, a mathematical representation of a biological neuron. This marked the beginning of neural networks, which are now fundamental to modern AI.

In 1950, Alan Turing released a paper titled “Computing Machinery and Intelligence”, suggesting a test for machine intelligence. This Turing test is still used today as a way to think about the evaluation of AI systems.

The term “artificial intelligence” was first introduced in 1956 during the Dartmouth Summer Research Project on Artificial Intelligence, marking the onset of AI research.

Numerous discoveries during this period spurred an AI boom in the 1960s, propelled by funding from the US Department of Defense for potential military applications. Leading figures like Herbert Simon and Marvin Minsky optimistically predicted that machines would achieve human-level intelligence within a generation. However, the intricacies of AI proved more challenging than anticipated, resulting in reduced funding and research, leading to what’s termed the “AI winter”.

The 1980s saw a revival in AI interest due to the commercial success of expert systems, which were rule-based systems emulating human reasoning. These systems found applications in diverse sectors, including healthcare and finance. Yet, this resurgence was temporary, with another “AI winter” setting in by 1987.

During the 90s and 2000s, machine learning (ML) became the predominant approach in AI. The amount of data that became available during this period was instrumental to the success of ML. Unlike traditional rule-based systems, ML algorithms discern patterns directly from data, leading to a range of applications such as, email spam filters, recommendation systems like Netflix, and financial forecasting. Machine learning shifted the focus of AI from rule-based systems to data-driven systems.

A significant shift occurred in 2012. Enhanced computational power (boosted by GPUs), data availability, and advancements in neural network algorithms gave rise to deep learning, a subset of ML. Deep learning quickly outpaced other ML techniques, leading to a surge in AI research, funding, and applications. By 2022, global investments in AI were approximately $91 billion, accompanied by a substantial increase in job opportunities and specialists.

Today, the applications of machine learning-based AI are ubiquitous, ranging from basic tasks like spam filtering to complex ones like autonomous vehicles and medical diagnostics. Generative AI has emerged as a subset of ML, and has garnered significant attention due to its ability to create content, such as images, videos, audio, and text.

What is Generative AI?

AI/ML engineers employ various tools and techniques to convert data into machine learning models, which then make predictions or categorizations. For instance, a model trained on an image dataset of cats and dogs can differentiate between the two based on learned patterns.

ML models cater to diverse applications: video security systems detect humans and potential break-ins, voice assistants like Siri and Alexa process speech to respond to user queries, autonomous vehicles identify objects and make decisions, and the healthcare sector utilizes ML to spot anomalies in medical images, among other uses.

Considering its pervasive use, let’s term this “traditional AI” or “traditional ML”. Such AI classifies or predicts content, taking an input to produce an output, such as identifying whether an image has a cat or dog, determining the best route to a destination, or estimating the likelihood of a tumor in an X-ray image.

Generative AI, a subset of ML, utilizes neural networks to create content. Trained on extensive datasets such as images, videos, audio, or text, these models generate new content based on identified patterns. Different generative AI models cater to varied content types: for instance, image generation models like OpenAI’s DALL-E rely on extensive image datasets, while text generation models like OpenAI’s ChatGPT are trained on vast text datasets.

Generative AI can craft a plethora of content. Image models can create diverse images, emulating specific artists or art movements. Similarly, text models can mimic specific authors or genres, producing text ranging from technical to creative, or even generate code in various programming languages.

Encountering generative AI models for the first time might seem magical. They appear to conjure the requested content out of thin air. Asking a text generation model to write a poem or a story, or an image generation model to create a painting or a photograph, can be a surreal experience. Consider the following examples:

The following images were generated using DALL-E, an image generation model. The model was prompted to “Generate an image of a bustling renaissance-era city populated by anthropomorphic animals.”

Notice the intricate details such as the ornate architecture and the different species of animals. In less than a minute, the model produced two unique images, each with a distinct style.

Text generation models are no less impressive. The following text was generated by ChatGPT, a text generation model. The model was prompted to “Create a single paragraph writing prompt about a house cat living in a post-apocalyptic world.”

In a post-apocalyptic world where cities lie in ruins and nature has started to reclaim its dominion, a lone house cat named Luna roams the desolate streets. Once pampered and loved in a warm home, Luna now relies on her heightened senses and feline agility to navigate the challenges of this new world. Amidst the silence, she discovers remnants of human civilization, encounters mutated creatures, and befriends unlikely allies, all while uncovering the mysteries of what led to the world’s end. As Luna’s journey unfolds, she becomes a symbol of hope and resilience, proving that even in the darkest times, life finds a way.

How do Generative AI models work?

It was once believed that only humans could create art, write stories, or compose music. These were thought to be uniquely human traits that would be difficult to replicate. Generative AI has proven otherwise. These models can create content that’s indistinguishable from human-crafted content. This begs the question: how do these models work?

Generative AI models are trained on vast datasets, learning patterns and relationships between data points. For instance, a text generation model learns the contextual relationships between words, while an image generation model learns the visual relationships between pixels. These models then utilize these learned patterns to generate new content.

Here’s a simplified analogy describing how a text generation model goes from training to generation.

Imagine an apprentice learning to cook by studying recipes. They’re asked to study a cookbook containing a diverse array of recipes, ranging from simple to complex. While studying the recipes, they learn the relationships between ingredients and cooking instructions. The more recipes they study, the more patterns they learn. They begin to build a mental model of the cooking process.

They notice that the mention of “chocolate” and “sugar” are often followed by a baking process. They notice that terms like “boil” are frequently succeeded by ingredients like “water” or “pasta”. Their mentor helps them to learn by asking them to predict what comes next in a recipe. The mentor validates or corrects their predictions. This iterative process of prediction and feedback, over countless recipes, refines their understanding and hones the accuracy of their predictions.

After all of this training, the mentor poses a challenge: “Craft a recipe for a chocolate cake.” The apprentice draws from all the recipes they’ve studied, and their finely-tuned understanding of the cooking process, to create an original recipe. The newly created recipe might draw inspiration from previous recipes, but it stands as a unique creation.

Generative AI models are trained in a similar manner. They’re given access to vast datasets, such as images, videos, audio, or text. They learn the patterns and relationships between data points, and utilize this knowledge to generate new content.

This is of course a simplified explanation of how generative AI models work. The actual process is more complex, involving intricate mathematical calculations and algorithms. However, the underlying principle remains the same: these models learn patterns from data, and utilize this knowledge to generate new content.

Misconceptions of Generative AI

The impressive capabilities of generative AI models are evident in the content they produce. This often leads to misconceptions about their design and capabilities. Let’s address some of these misconceptions.

Are Generative AI models becoming self-aware?

Absolutely not. These models neither think nor feel and lack understanding of their generated content or the surrounding world. They are as self-aware as your toaster, with no architectural provision for anything resembling self-awareness. Give the right prompt and parameters, they may generate content that appears self-aware, but this is merely a reflection of their training data.

Are Generative AI models unbiased?

Unambiguously, no. These models are trained on vast amounts of human-generated data. Much of the data is sourced from the internet, infamous for its incivility, misinformation, and toxicity. These models mirror the biases of their training data, reflecting both subtle and glaring biases. Placing blind trust in these models is similar to trusting random people on the internet. The information they provide might be accurate, but it’s prudent to be skeptical.

Are Generative AI models accurate?

It depends on the use case. These models can be used to provide accurate information, however, they can also completely fabricate information. Any technology that can generate fiction from thin air has the ability to produce misinformation that appears authentic. This is a lesson that some people have learned the hard way. Always verify information from trusted sources.

Will AI replace my job?

The honest answer is, maybe. It depends on your profession. Starting in the 16th century, lamp lighters would light street lamps at dusk and extinguish them at dawn. Electricity and the light bulb made this profession obsolete. Similarly, automobiles made horse-drawn carriages obsolete. Historically, innovations such as these rendered certain jobs obsolete, while creating new ones. AI will likely reduce manpower for certain tasks, allowing fewer people to accomplish more. However, it will also create new jobs, requiring new skill sets, such as the emerging field of prompt engineering. As history consistently reveals, change is the only constant and adaptability is the key.

This section merely skims the surface of prevalent misconceptions surrounding generative AI. Recognizing their current usage and inevitable evolution is crucial. Grasping their capabilities and constraints will guide informed utilization.

What are the ethical concerns with Generative AI?

Generative AI’s capability to produce content on such a grand scale amplifies existing ethical dilemmas, while introducing new ones. Let’s explore some of these concerns further.

Cybersecurity

Generative AI models can be used to bypass CAPTCHAs and other security measures, potentially leading to increased cyberattacks. They can also be used to create deepfakes, which are synthetic media that appear authentic, potentially leading to misinformation and propaganda. The ability for these models to generate code in a wide range of programming languages can be used to perform automated and AI-assisted hacking.

Bias and Discrimination

Generative AI models will perpetuate and amplify societal biases present in their training data, leading to unfair or discriminatory outputs. Unchecked, this will lead to a feedback loop, where biased outputs are used as training data for future models, further amplifying the bias. The use of generative AI to make decisions that impact people’s lives, such as public policy, hiring, or criminal justice, can lead to unfair or discriminatory outcomes.

Misinformation and Fake News

Generative AI models can be used to create fake news, propaganda, or other forms of misinformation, and on an unprecedented scale. This can be used to influence public opinion, sway elections, or even incite violence. These are particularly concerning when coupled with deepfakes, which can be used to create fake videos of public figures.

Privacy

Vendor-provided models may be trained on either or both public and private data. The use of private data, such as medical records, can result in models inadvertently revealing sensitive information. The use of public data, such as social media posts, can be used to infer private information about individuals, leading to privacy violations or even blackmail.

Intellectual Property

Generative AI is moving faster than the legal system, raising questions about the ownership and rights to content generated by AI. This is especially concerning for content creators, such as artists, musicians, and writers, whose livelihoods depend on their creations.

De-personalization

Generative AI models are and will continue to be used to replace certain human interactions. Chatbots are already replacing customer service representatives, and this trend will likely continue in other areas. This can lead to a de-personalization of human interactions, potentially leading to reduced empathy and compassion.

Safety and Reliability

The use of generative AI in critical applications, such as autonomous vehicles, medical diagnostics, or military applications, raises concerns about safety and reliability. Unpredictable outputs can lead to accidents, injuries, or even loss of life. The use of generative AI in critical applications requires careful consideration and extensive testing.

Transparency and Accountability

Generative AI models are often black boxes, making it difficult to understand how they work or why they make certain decisions. These models commonly produce different outputs for the same input, making it difficult to predict their behavior. Who is responsible when a generative AI model makes a mistake? How can we ensure that these models are used responsibly? These are questions that require careful consideration and research.

This section merely scratches the surface of ethical concerns surrounding generative AI. Recognizing these concerns is crucial to understanding the technology’s impact on our lives.

Use cases of Generative AI

Let’s explore some of the different industries and use cases where generative AI is being, or has the potential to be, applied.

Technology

Generative AI has become an invaluable addition to the software development workflow. It’s used to generate code, automate testing, generate documentation, explain code, and modernize legacy systems. It’s also being used for a range of cybersecurity applications, such as, automated hacking, malware detection, intrusion detection, and vulnerability detection.

Finance

Generative AI gains its power from vast amounts of data. Making the data-rich finance sector a natural fit for generative AI. It can be used to automate financial analysis, enhance risk mitigation, and optimize operations. It can also be used to generate content, such as summaries, and convert text to charts.

Ecommerce

Generative AI is being used to improve customer engagement and operational efficiency. It can offer real-time customer insights and refine the shopping experience. It can be used to create chatbots and virtual agents that serve as front-line customer service representatives, providing 24/7 support. It can also produce and update product descriptions and marketing content.

Healthcare

Generative AI is being applied to a wide range of healthcare use cases, including medical imaging, drug discovery, and patient care. It’s being used to analyze medical images, identify anomalies, and predict disease progression. It’s also being used to discover new drugs and treatments, and to optimize patient care.

Education

Generative AI is being used for automating plagiarism detection, generating practice problems, and providing student feedback. It can also be used to create personalized learning experiences, such as virtual tutors, and to generate educational content. This has the potential to transform the way students learn and engage with content.

Automotive and Manufacturing

Generative AI is being used to enhance vehicle design, engineering and manufacturing processes, and the development of autonomous vehicles. Companies such as Toyota, Mercedes-Benz, and BMW are leveraging AI to streamline workflows, improve productivity, and drive innovation.

Entertainment and Media

Generative AI has the potential to disrupt and transform the entertainment industry. It can be used to create content, such as music, movies, and video games. It can enhance existing forms of entertainment and create new forms. However, it also raises significant ethical concerns, such as the potential for misuse and intellectual property issues.

Legal

Generative AI has found a range of applications in the legal sector, including contract review and legal research. It can enable legal professionals to focus on higher-level tasks, by automating time consuming and repetitive tasks. While it can save time and effort, the potential for generative AI to produce fabricated information requires careful consideration and oversight.

Urban Planning

Generative AI has a wide range of use cases in urban planning, including optimizing traffic flow, improving disaster preparedness, optimizing for sustainable growth, and enhancing accessibility and safety in urban spaces. Companies like Digital Blue Foam are already providing AI-driven tools for urban planning.

Agriculture

Generative AI shows a lot of promise for farming and agriculture. It can be used to optimize crop yields, reduce pesticide use, prevent crop losses, and even design plant-based proteins. This can help to ensure food security and reduce environmental impact.

Environmental Science

Generative AI is already being used in environmental science, with use cases ranging from climate change modeling to pollution control. It’s being used to analyze environmental data, predict environmental changes, and inform environmental policy. However, generative AI itself has a significant carbon footprint, especially when training large models. This is a growing concern that must be addressed in order to ensure that AI can be part of a sustainable future.

This non-exhaustive list of use cases provides a glimpse into the potential of generative AI.

What does the future of Generative AI look like?

While it seems clear that generative AI is here to stay, its future is less certain. The technology is evolving rapidly, with new models and applications emerging. However, the current state may provide some insights into the future. So, let’s speculate about the future of generative AI.

Generative AI will continue to see enhancements in quality and efficiency. Text and image models are already producing human-like content, with audio models catching up and video models evolving steadily.

Currently, the high costs and sophisticated hardware requirements restrict the accessibility of these models. Small and less capable models can already run on certain mobile devices. However, advancements in hardware and model design will likely democratize access, paving the way for more widespread applications.

Generative AI will likely become more interactive, with models responding to user feedback, adapting to user preferences, and even learning from user interactions. This will enable more personalized experiences, with models catering to individual preferences. This will change the way we interact with software and services, with natural language-based interactions becoming the norm.

Different types of content are currently generated at different speeds, with text being the fastest and video being the slowest. A wide range of unprecedented applications will likely emerge once generative AI models are able to generate content in real-time.

The incorporation of generative AI into video game engines will lead to more immersive and interactive experiences. Entire game worlds could be generated on-the-fly, with the environment adapting to player actions. Breaking away from the traditional linear narrative, games could evolve into unique experiences with every play through. Players could interact with virtual characters, potentially indistinguishable from real people.

Streaming services will likely utilize generative AI to produce content on-the-fly, based on viewer preferences. This could reshape the entertainment sector, with digital characters emerging as the new celebrities. Advertisements and product placements could be added and removed in real-time.

Innovative learning methods will likely emerge, with students interacting with digital tutors for personalized, pace-adjusted learning. They could even converse with virtual renditions of historical figures. Information could be presented in a variety of formats, such as text, audio, or video, based on individual preferences.

Advancements in augmented reality (AR) and virtual reality (VR) will provide new avenues for generative AI. AR and VR will likely become more immersive, with generative AI models creating content in real-time. This could lead to new forms of entertainment, such as interactive movies and new forms of art.

Ultimately, the quality of underlying training data might be the distinguishing factor between models. The role of traditional content creators may shift towards creating, curating, and maintaining training data. Keeping training data fresh and relevant could ensure that dynamically generated content remains accurate and up-to-date. High-quality training data will likely become a competitive advantage.

While this is all speculative, the foundation for this future is already being laid. These and other advancements may occur sooner than you might expect.

Conclusion

Generative AI, now mainstream, is poised for rapid evolution. It’s already transforming the way we interact with software and services, and will likely continue to do so. The technology is still in its infancy, with many unanswered questions and ethical concerns. However, it’s here to stay, and will likely become more pervasive in our lives. The best way to prepare for the impact of generative AI is to understand the technology and its capabilities.

Learn Generative AI on Cloud Academy!

If you want to deep dive into AI, Cloud Academy has a whole dedicated section in its training library. Also, if you’re looking to channel the power of AI in your business, request a free demo today!

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What is OpenAI?

OpenAI is an artificial intelligence research organization that focuses on developing advanced AI technologies. It is known for creating state-of-the-art language models like GPT-3 and GPT-4, which can understand and generate human-like text. OpenAI’s mission is to ensure that artificial general intelligence (AGI) benefits all of humanity and to promote the responsible and safe development of AI technologies. It conducts research, develops AI systems, and collaborates with partners to achieve its goals.

Introducing GPT-4 Turbo

GPT-R Turbo will offer a 128K context window versus 8K and 32K for its previous versions. The context window refers to the amount of text or information that the model considers when generating a response. A larger context window allows ChatGPT to retain more information from previous questions.   A 128K context window would be roughly 300 pages of text. GPT-4 turbo will be priced significantly cheaper. Input tokens are three times cheaper. Output tokens are two times cheaper than the previous version. Tokens are used to represent pieces of words. To learn more about tokens, watch Cloud Academy’s course ChatGPT Prompts, Completions, & Tokens that cover this subject.

Other notable features include the following: 

• New Assistant APIs that make it easier for developers to build their own assistive AI applications.
• DALL-E 3 can be integrated into applications by developers
• Developers using text can generate human-quality speech via the text-to-speech API

While there are other new features, OpenAI’s announcement of Copyright Shield is more geared towards the peace of mind of their enterprise customers. OpenAI will step in and defend its customers over legal claims around copyright infringement and pay the costs incurred. This matches Google and Microsoft policies regarding copyright infringement claims targetting customers using those companies’ Gen AI tools.

OpenAI (and beyond) training on Cloud Academy

If you’re interested in artificial intelligence, OpenAI, and similar services you should check out our training library. Here’s something you may like:

• ChatGPT Prompts, Completions, & Tokens course
• What is Generative AI course
• Generative AI Concerns and Ethics course
• And much more

To stay updated on related news, releases and new training about OpenAI and artificial intelligence in general, keep an eye on the Cloud Academy’s blog and training library.

The post OpenAI announces GPT-4 Turbo appeared first on Cloud Academy.

If you want to deep dive into AI, Cloud Academy has a whole dedicated section in its training library. Also, if you’re looking to channel the power of AI in your business, request a free demo today!

Key takeaways

• AI is a technology divided into multiple categories based on its functionality, learning capabilities and application.
• Natural Language Processing (NLP), Computer Vision and Robotics are some of the most prominent uses for AI.
• Current trends in AI point to great potential but responsible use must be ensured through consideration of ethical implications.

Introduction to AI

Artificial intelligence involves building smart machines from vast datasets, including the development of artificial intelligence systems. It aims to replicate human intelligence and perform tasks that would otherwise require our input, such as decision-making, object recognition, and problem-solving. The term artificial intelligence also refers to AI systems integrating previous knowledge and experiences to speed up and enhance the accuracy and efficiency of human efforts. With the use of complex algorithms and methods, machines can make independent decisions that revolutionize industries and alter our lifestyle and work habits.

Machine learning and deep learning, two subfields of AI, lie at the heart of this technology, utilizing complex algorithms and neural networks to empower machines to learn and adapt. This blog post will explore the different types of AI – Narrow AI, General AI, and Superintelligent AI, along with their applications across various sectors.

Types of AI

The capabilities of AI systems can be classified into three primary categories: Narrow AI, General AI, and Superintelligent AI. Narrow AI, also known as Weak AI, focuses on performing specific tasks without the ability to learn beyond its intended purpose.

General AI, or Strong AI, possesses human-like intelligence, capable of executing multiple tasks simultaneously. Lastly, Superintelligent AI surpasses human intelligence, performing any task better than humans. Let’s examine each of these AI types and their distinct capabilities more closely.

Narrow AI (Artificial Narrow Intelligence or Weak AI)

Artificial Narrow Intelligence (ANI) is a type of Artificial Intelligence which mainly focuses on executing specific commands. These AI tools can perform proficient tasks as per the instructions provided to them. These systems fulfill particular tasks without the capacity to learn beyond their intended purpose, such as image recognition software, self-driving cars, and AI virtual assistants like Siri. Although Narrow AI has made significant advancements in recent years, it is not without its drawbacks.

The limitations of Narrow AI include:

• Lack of flexibility
• Incomplete comprehension of context
• Incapacity to adapt and learn
• Reliance on data

Despite these shortcomings, Narrow AI continues to play an essential role in many AI applications, providing practical solutions to everyday problems and enhancing user experiences.

General AI (Artificial General Intelligence or Strong AI)

Artificial General Intelligence (AGI) is a more advanced form of AI, capable of learning, thinking, and carrying out a vast array of tasks in a manner comparable to humans. The objective of designing AGI is to create machines that can execute multifaceted duties and serve as lifelike, intellectually comparable assistants to people in daily life. However, we are still considerably distant from constructing an AGI system.

The realization of AGI requires the development and refinement of fundamental technologies, such as supercomputers, quantum hardware, and generative AI models like ChatGPT. As researchers continue to push the boundaries of AI, the prospect of achieving General AI remains an exciting and significant milestone in the field.

Superintelligent AI

Super AI, or Artificial Superintelligence (ASI), is the theoretical level of AI wherein its capabilities exceed that of human intelligence, and it attains self-awareness. These hypothetical AI systems possess the potential to become the most proficient form of intelligence on the planet, outstripping human intelligence and being markedly better at all tasks we undertake.

The concept of self-aware AI raises ethical concerns and debates surrounding the creation of sentient AI. While the idea of superintelligent AI might sound like science fiction, it serves as a reminder that as AI research and development continues to advance, potential risks and ethical implications must be carefully considered and addressed.

AI based on functionality

Another way to classify AI systems is based on their functionalities, which can be divided into categories such as types of artificial intelligence:

1. Reactive Machines: These AI systems perform tasks based on current data without learning from past experiences.
2. Limited Memory AI: These AI systems utilize past data to make informed decisions and enhance their performance over time.
3. Theory of Mind AI: These AI systems focus on understanding and interpreting the mental states of other agents.

Let’s examine each of these functional classifications of AI in greater detail.

Reactive Machines

Reactive Machines are basic AI systems that solely operate on current data and execute specific tasks without gaining knowledge from past experiences. Examples of reactive machines include:

• IBM’s Deep Blue, which defeated chess grandmaster Garry Kasparov in 1997
• AI systems used for filtering out spam from email inboxes
• AI systems used for recommending movies based on recent Netflix searches

Reactive machines can be employed for executing fundamental autonomous processes.

Despite their usefulness in certain applications, reactive machines have limitations. They don’t allow for learning or adaptation; they can only recognise and respond to a certain amount of data. Consequently their functionality is limited in comparison to those that have the ability to learn and improve. Moreover, they are unable to build upon previous knowledge or perform complex tasks that require learning and adaptation.

Limited Memory AI

Limited Memory AI systems, as the name suggests, utilize past data to make informed decisions and enhance their performance over time. By observing other vehicles’ speed and direction, self-driving cars can navigate the road and adjust accordingly. This kind of AI evolves over time as it is taught on more data, making it more advanced than reactive machines.

Limited Memory AI has found applications in various sophisticated use cases, such as chatbots, virtual assistants, and natural language processing. These systems demonstrate the potential of AI to learn from past experiences and improve their capabilities over time, offering more flexible and adaptable solutions compared to reactive machines.

Theory of Mind AI

Theory of Mind AI is an advanced category of AI systems that focus on comprehending and interpreting the human mind, including emotions, beliefs, and intentions. These systems are still under research and development, with the goal of enabling AI to better understand and interact with humans and other agents. For example, a self-driving car that is aware of a neighbor’s child playing near the street after school would naturally reduce speed when passing that neighbor’s driveway – something a basic limited memory AI would be unable to do.

Despite the potential benefits of Theory of Mind AI in various applications, there are concerns and challenges to consider. Emotional cues are highly complex, and it may take a significant amount of time for AI machines to master them, leading to potential errors during the learning stage.

Additionally, once technologies can detect and respond to emotional signals, it could result in the automation of certain occupations, raising ethical and societal questions.

AI based on learning capabilities

The learning capabilities of AI systems can be classified into categories such as:

1. Machine Learning: This allows us to give machines the ability to interpret, process, and analyze data, helping them solve real-world problems.
2. Deep Learning: A subset of Machine Learning, it utilizes artificial neural networks to acquire knowledge from data.
3. Reinforcement Learning: Another type of Machine Learning, it uses rewards and punishments to acquire knowledge from its environment.

Let’s further investigate each of these classifications based on learning capabilities.

Machine Learning

Machine Learning is an AI system that acquires knowledge from data in order to generate predictions and decisions. It operates by examining data and recognizing patterns or associations within the data. Machine Learning algorithms use techniques to estimate a target function and predict output variables based on input variables. By inputting training data into the algorithm, it learns from the data to generate a model that can make predictions or identify patterns in new data.

Machine Learning algorithms reduce human intervention and can be utilized for a broad range of tasks involving data analysis and pattern recognition. They are essential in many AI applications, including image recognition, natural language processing, and self-driving cars. As more data becomes available, Machine Learning models continue to improve their predictions and decision-making capabilities.

Deep Learning

Deep Learning is an advanced AI system that utilizes neural networks to solve intricate problems, such as image recognition and natural language processing. It is a subset of Machine Learning centered around the application of artificial neural networks with multiple layers. Deep learning neural networks possess multiple layers, comprising input and output layers, and are able to carry out complex operations such as representation and abstraction.

Examples of Deep Learning applications include facial recognition algorithms on Facebook, self-driving cars, and virtual assistants such as Siri and Alexa. As Deep Learning continues to advance and mature, it is expected to play an increasingly significant role in AI research and development, unlocking new possibilities and applications across various industries.

Reinforcement Learning

Reinforcement Learning is an AI system that learns through trial and error, optimizing its actions to achieve specified objectives. It involves:

• Establishing a system of rewarding desired behaviors
• Punishing negative behaviors
• Learning the most efficient behavior in an environment
• Acquiring the maximum reward

Reinforcement Learning focuses on learning optimal actions through exploration and exploitation, distinguishing itself from other machine learning methods like supervised and unsupervised learning.

Noteworthy examples of Reinforcement Learning in practical applications include:

• Automated robots
• Natural language processing
• Marketing and advertising
• Image processing
• Game optimization and simulation
• Self-driving cars
• Industry automation
• Finance and economics
• Healthcare
• Broadcast journalism

As Reinforcement Learning algorithms continue to improve, they are expected to play a crucial role in the future development of AI systems and their applications.

AI based on application

AI systems and their applications can be found in various industries, transforming the way we live and work. From healthcare to banking and finance, marketing, and entertainment, AI is employed across industries worldwide, offering innovative solutions and enhancing user experiences.

Some examples of AI applications in everyday life include Google’s predictive search algorithm, Netflix’s movie recommendation system, and Facebook’s facial recognition tagging system. As AI continues to advance, its applications are expected to grow exponentially, with new use cases emerging in diverse sectors and industries.

Natural Language Processing (NLP)

Natural Language Processing (NLP) is a branch of AI that facilitates machines to comprehend and process human language. NLP has applications in enhancing user experience and communication, with potential use cases in various industries and fields.

In the following subsections, two specific applications of NLP – Computer Vision and Robotics, will be discussed.

Computer Vision

Computer Vision is an AI system that enables machines to interpret and analyze visual information from the world, such as images and videos. It utilizes pattern recognition algorithms to educate computers to interpret and comprehend the visual world, analogous to how the human brain comprehends visual information. By employing computer vision systems, we can enable applications such as:

• Facial recognition
• Object detection and tracking
• Image and video analysis
• Autonomous vehicles

The potential of computer vision is vast, with applications ranging from security and surveillance to healthcare and entertainment. As computer vision technology continues to advance, we can expect to see even more innovative and impactful applications emerge in the future.

Robotics

Robotics is the field of AI that incorporates AI systems into robots to perform tasks autonomously. By integrating AI into robots, they can explore their environment, identify and recognize objects, and handle objects without human interference. AI empowers robots with capacities like spatial relations, computer vision, and motion control, enabling them to carry out tasks that necessitate intelligence and adaptability.

Robotics has applications in various industries, including manufacturing, healthcare, and service industries. As AI research and development continue to progress, we can expect to see increasingly advanced and capable robots emerging, with the potential to revolutionize the way we work and live.

Current and Future Trends

The current state of AI research and development is at its peak, with daily breakthroughs being made and new technologies emerging. AI has the potential to augment efficiency, productivity, and accuracy in various sectors, with predictions suggesting that it may drive revenue and profit growth, doubling economic growth rates by 2035 and generating trillions of dollars in value.

With the ongoing advancement of AI, researchers are striving to develop basic versions of self-aware AI, extending the limits of possibilities and prompting significant ethical and societal discussions. The future of AI holds exciting and transformative possibilities, but it is crucial to consider the potential risks and ethical implications that come with these advancements.

Conclusion

This blog post has covered a broad range of AI types, their applications, and potential influence on society and future technology. From Narrow AI systems designed for specific tasks to the hypothetical Superintelligent AI, the capabilities and potential of artificial intelligence are vast and ever-evolving.

While we continue to expand the limits of AI research and development, we must take into account the ethical and societal consequences of these advancements, aiming for a future where AI enhances every aspect of our lives.

Summary

In conclusion, the realm of AI is vast and complex, encompassing various types, functionalities, and learning capabilities. From enhancing user experiences in everyday life to revolutionizing industries, AI holds the key to transformative advancements in technology and society. As we continue to explore and unlock the potential of AI, it is crucial to consider the ethical implications and potential risks associated with these powerful technologies, ensuring a future where AI serves the greater good.

To stay up to date on the AI world, keep an eye on the Cloud Academy’s blog.

Frequently Asked Questions

What are the 4 main types of AI?

AI can be categorized into four primary types: reactive, limited memory, theory of mind and self-aware.

What type of AI is Siri?

Siri is an example of conversational AI, utilizing machine learning and natural language processing to respond to queries.

What is the difference between Narrow AI and General AI?

Narrow AI is designed to do one task efficiently, while General AI can solve complex problems by mimicking human intelligence.

How does Reinforcement Learning differ from other machine learning methods?

Reinforcement Learning focuses on finding optimal actions, making it distinct from other machine learning methods such as supervised and unsupervised learning.

What are some current and future trends in AI research and development?

AI research and development is a growing field, with job openings increasing and projections that AI will double economic growth rates by 2035. It is expected to generate trillions of dollars in value.

Learn more on AI with Cloud Academy!

If you want to deep dive into AI, Cloud Academy has a whole dedicated section in its training library. Also, if you’re looking to channel the power of AI in your business, request a free demo today!

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