Many people interested in AI for business feel overwhelmed by the technical jargon and complexity surrounding this technology. This blog will help you cut through the noise and understand how to build AI to implement a fully functional system tailored to your business goals.
Lamatic's managed generative AI tech stack can help you achieve your objectives by providing the tools and templates you need to confidently design, develop, and implement a fully functional AI system from the ground up, even with limited prior experience.
What is Artificial Intelligence?
Artificial intelligence refers to machines' simulation of human intelligence, particularly computer systems. AI enables computers and machines to perform tasks that typically require human intelligence. Applications and devices equipped with AI can see and identify objects. They can understand and respond to human language. They can learn from new information and experience. They can make detailed recommendations to users and experts. They can act independently, replacing the need for human intelligence or intervention (a classic example being a self-driving car).
But in 2024, most AI researchers, practitioners, and AI-related headlines focus on generative AI (gen AI) breakthroughs. This technology can create original text, images, video, and other content. To fully understand generative AI, it’s important to understand the technologies on which generative AI tools are built: machine learning (ML) and deep learning.
The History of AI: Milestones in a Changing Field
The idea of a machine that thinks dates back to ancient Greece. But since the advent of electronic computing (and relative to some of the topics discussed in this article) important events and milestones in the evolution of AI include the following:
1950
Alan Turing publishes Computing Machinery and Intelligence (link resides outside ibm.com). In this paper, Turing, famous for breaking the German ENIGMA code during WWII and often called the father of computer science, asks, Can machines think? From there, he offers a test, now famously known as the Turing Test, where a human interrogator would try to distinguish between a computer and a human text response.
While this test has undergone much scrutiny since it was published, it remains an important part of the history of AI and an ongoing concept within philosophy as it uses ideas around linguistics.
1956
John McCarthy coined the term artificial intelligence at the first-ever AI conference at Dartmouth College. (McCarthy went on to invent the Lisp language.) Later that year, Allen Newell, J.C. Shaw, and Herbert Simon created the Logic Theorist, the first-ever running AI computer program.
1967
Frank Rosenblatt built the Mark 1 Perceptron, the first computer based on a neural network that learned through trial and error. Just a year later, Marvin Minsky and Seymour Papert published a book titled Perceptrons, which became both the landmark work on neural networks and, at least for a while, an argument against future neural network research initiatives.
1980
Neural networks, which use a backpropagation algorithm to train itself, became widely used in AI applications.
1995
Stuart Russell and Peter Norvig published Artificial Intelligence: A Modern Approach (link resides outside ibm.com), which became one of the leading textbooks in the study of AI. In it, they delve into four potential goals or definitions of AI, which differentiate computer systems based on rationality and thinking versus acting.
1997
IBM's Deep Blue beat then-world chess champion Garry Kasparov in a chess match (and rematch).
2004
John McCarthy writes a paper, What Is Artificial Intelligence? (link resides outside ibm.com), and proposes an often-cited definition of AI. By this time, the era of big data and cloud computing is underway, enabling organizations to manage ever-larger data estates, which will one day be used to train AI models.
2011
IBM Watson® beats champions Ken Jennings and Brad Rutter at Jeopardy! Also, data science begins to emerge as a popular discipline around this time.
2015
Baidu's Minwa supercomputer uses a special deep neural network called a convolutional neural network to identify and categorize images with a higher accuracy rate than the average human.
2016
DeepMind's AlphaGo program, powered by a deep neural network, beats Lee Sodol, the world champion Go player, in a five-game match. The victory is significant given the huge number of possible moves as the game progresses (over 14.5 trillion after just four moves). Later, Google purchased DeepMind for a reported USD 400 million.
2022
A rise in large language models or LLMs, such as OpenAI’s ChatGPT, is creating an enormous change in AI performance and its potential to drive enterprise value. Deep-learning models can be trained on large amounts of data with these new generative AI practices.
2024
The latest AI trends point to a continuing AI renaissance. Multimodal models that can take multiple data types as input provide richer, more robust experiences. These models bring together computer vision image recognition and NLP speech recognition capabilities. Smaller models are also making strides in an age of diminishing returns with massive models with large parameter counts.
Machine Learning vs. Deep Learning: What’s The Difference?
Directly underneath AI, we have machine learning, which involves creating models by training an algorithm to make predictions or decisions based on data. It encompasses a broad range of techniques that enable computers to learn from and make inferences based on data without being explicitly programmed for specific tasks. Many machine learning techniques or algorithms exist, including:
- Linear regression
- Logistic regression
- Decision trees
- Random forest
- Support vector machines (SVMs)
- K-nearest neighbor (KNN)
- Clustering and more
Neural Networks and Supervised Learning
Each approach is suited to different kinds of problems and data. But one of the most popular types of machine learning algorithms is a neural network (or artificial neural network). Neural networks are modeled after the human brain's structure and function. They consist of interconnected layers of nodes (analogous to neurons) that work together to process and analyze complex data. Neural networks are well suited to tasks that involve identifying complex patterns and relationships in large amounts of data.
Supervised Learning
The simplest form of machine learning is supervised learning, which involves using labeled data sets to train algorithms to classify data or predict outcomes accurately. In supervised learning, humans pair each training example with an output label. The model aims to learn the mapping between inputs and outputs in the training data to predict new, unseen data labels.
What is Deep Learning?
Deep learning is a subset of machine learning that uses multilayered neural networks, called deep neural networks, that more closely simulate the complex decision-making power of the human brain. Deep neural networks include an input layer, at least three but usually hundreds of hidden layers, and an output layer, unlike neural networks used in classic machine learning models, which usually have only one or two hidden layers.
These multiple layers enable unsupervised learning:
- They can automate the extraction of features from large
- Unlabeled and unstructured data sets
- Make their predictions about what the data represents
Because deep learning doesn’t require human intervention, it enables machine learning at a tremendous scale. It is well suited to natural language processing (NLP), computer vision, and other tasks involving fast, accurate identification of complex patterns and relationships in large amounts of data. Some form of deep learning powers most artificial intelligence (AI) applications in our lives today.
Deep learning also enables the following:
- Semi-supervised learning: combines supervised and unsupervised learning by using labeled and unlabeled data to train AI models for classification and regression tasks. Self-supervised learning generates implicit labels from unstructured data rather than relying on labeled data sets for supervisory signals.
- Reinforcement learning: which learns by trial-and-error and reward functions rather than by extracting information from hidden patterns.
- Transfer learning: knowledge gained through one task or data set is used to improve model performance on another related task or different data set.
What is Generative AI?
Generative AI, sometimes called gen AI, refers to deep learning models that can create complex original content, such as long-form text, high-quality images, realistic video or audio, and more, in response to a user’s prompt or request.
At a high level, generative models encode a simplified representation of their training data and then draw from that representation to create new work similar to the original data but not identical. Generative models have been used for years in statistics to analyze numerical data. However, over the last decade, they have evolved to analyze and generate more complex data types. This evolution coincided with the emergence of three sophisticated deep-learning model types:
- Variational autoencoders, or VAEs: were introduced in 2013 and enabled models that could generate multiple variations of content in response to a prompt or instruction.
- Diffusion models: first seen in 2014, which add "noise" to images until they are unrecognizable and then remove the noise to generate original images in response to prompts.
- Transformers (also called transformer models): are trained on sequenced data to generate extended sequences of content (such as words in sentences, shapes in an image, video frames, or commands in software code). Transformers are at the core of most of today’s headline-making generative AI tools, including ChatGPT and GPT-4, Copilot, BERT, Bard, and Midjourney.
Why is Artificial Intelligence Important?
Artificial intelligence is transforming everyday life and business operations. AI can analyze data at incredible speeds, making it easier for humans to solve complex problems and automate repetitive tasks. Some AI systems can even perform these functions independently without human intervention.
Organizations can eliminate mundane activities to boost productivity and empower employees to focus on high-value work that drives business growth. At home, AI-enabled devices like smart speakers and security systems learn user preferences over time to make life easier and improve home safety. AI can analyze customer data to personalize interactions and improve services for better satisfaction and retention.
End-to-End Efficiency
AI improves operations by eliminating friction across processes to enhance analytics and resource utilization, significantly reducing costs. It can also automate complex processes and minimize downtime by predicting maintenance needs.
Improved Accuracy and Decision-Making
AI augments human intelligence with rich analytics and pattern prediction capabilities to improve employee decisions' quality, effectiveness, and creativity.
Intelligent Offerings
Machines think differently than humans. They can uncover gaps and opportunities in the market more quickly, helping you introduce new products, services, channels, and business models with speed and quality that wasn’t possible before.
Automating Repetitive Tasks
AI technology can automate repetitive tasks such as data entry, factory work, and customer service conversations, allowing humans to focus on other priorities.
Solving Complex Problems
AI’s ability to process large amounts of data at once allows it to quickly find patterns and solve complex problems that may be too difficult for humans, such as predicting financial outlooks or optimizing energy solutions.
Empowered Employees
AI can tackle mundane activities while employees spend time on more fulfilling high-value tasks. By fundamentally changing the way work is done and reinforcing the role of people to drive growth, AI is projected to boost labor productivity. Using AI can also unlock the incredible potential of disabled talent while helping all workers thrive.
Superior Customer Service
Continuous machine learning provides a steady flow of 360-degree customer insights for hyper-personalization. From 24/7 chatbots to faster help desk routing, businesses can use AI to curate real-time information and provide high-touch experiences that drive growth, retention, and overall satisfaction.
AI is used in many ways, but the prevailing truth is that your AI strategy is your business strategy. To maximize your return on AI investments, identify your business priorities and determine how AI can help.
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How Does AI Work?
AI works by combining large data sets with intuitive processing algorithms. AI can manipulate these algorithms by learning behavior patterns within the data set. It’s crucial to understand that AI is not just one algorithm. Instead, it is a machine learning system that can solve problems and suggest outcomes. Let’s look at how AI works step-by-step.
What are the Steps Involved in AI Processing?
1. Input
The first step of AI is input. An engineer must collect the data for AI to perform properly. Data does not necessarily have to be a text input; it can also be images or speech. It’s important to ensure the algorithms can read inputted data. It’s also necessary to clearly define the context of the data and the desired outcomes in this step.
2. Processing
The processing step is when AI takes the data and decides what to do with it. While processing, AI interprets the pre-programmed data and uses the behaviors it has learned to recognize the same or similar behavior patterns in real-time data, depending upon the particular AI technology.
3. Data Outcomes
After AI technology has processed the data, the outcomes are predicted. This step determines if the data and its given predictions are a failure or a success.
4. Adjustments
If the data set produces a failure, AI technology can learn from the mistake and repeat the process differently. The algorithms’ rules may need to be adjusted or changed to fit the data set. Outcomes may also shift during adjustment to reflect a more desired or appropriate outcome.
5. Assessments
Once AI has finished its assigned task, the last step is assessment. The assessment phase allows the technology to analyze the data and make inferences and predictions. It can also provide necessary, helpful feedback before rerunning the algorithms.
How Does Generative AI Work?
Generative AI operates in three phases:
- Training to create a foundation model.
- Tuning to adapt the model to a specific application.
- Generation, evaluation, and more tuning to improve accuracy.
Training
Training Generative AI begins with a foundation, a deep learning model that is the basis for multiple generative AI applications. Today's most common foundation models are large language models (LLMs) created for text generation applications. However, there are foundation models for:
- Image
- Video
- Sound, music generation
- Multimodal foundation models
That supports several kinds of content. To create a foundation model, practitioners train a deep learning algorithm on huge volumes of relevant raw, unstructured, unlabeled data, such as:
- Terabytes or petabytes of data
- Text images
- Video from the internet
The training yields a neural network of billions of parameters encoded representations of the data's entities, patterns, and relationships that can generate content autonomously in response to prompts. This is the foundation model. This training process is compute-intensive, time-consuming, and expensive. It requires thousands of clustered graphics processing units (GPUs) and weeks of processing, typically costing millions of dollars. Open-source foundation model projects like Meta's Llama-2 enable gen AI developers to avoid this step and its costs.
Tuning
Tuning the model must be tuned to a specific content generation task. This can be done in various ways, including:
- Fine-tuning: This involves feeding the model application-specific labeled data, questions, or prompts the application will likely receive and corresponding correct answers in the desired format.
- Reinforcement learning with human feedback (RLHF): Involves human users evaluating the accuracy or relevance of model outputs so that the model can improve itself. This can be as simple as having people type or talk back corrections to a chatbot or virtual assistant.
Generation
Generation, evaluation, and more tuning Developers and users regularly assess the outputs of their generative AI apps and further tune the model even as often as once a week for greater accuracy or relevance. The foundation model is updated less frequently, every year or 18 months.
Another option for improving a gen AI app's performance is retrieval augmented generation (RAG), a technique for extending the foundation model to use relevant sources outside the training data to refine the parameters for greater accuracy or relevance.
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10 Best Practices for Securely Developing With AI
1. Beware of Prompt Injection Attacks
Applications that leverage AI and huge language models can be vulnerable to prompt injection attacks. This occurs when an attacker manipulates the prompts given to the AI model to elicit a response that benefits the attacker.
For example, suppose a chatbot in your application has been trained to help users with specific tasks or answer questions. An attacker may try to manipulate the input to the AI model by injecting malicious prompts that make the chatbot reveal sensitive information about other users. Safeguarding against such attacks is crucial to protecting the integrity of your application and your users’ data.
2. Limit Access to Data
When developing AI applications, it’s important to restrict access to sensitive data. Large language models, or LLMs, often need to read and manipulate data. The best practice is to provide LLMs with only the data necessary to perform their functions. If the LLM must access sensitive data, ensure strong security controls are in place to protect the information. Implement checks before and after LLM interactions to validate that the requested and returned data is appropriate.
3. Understand the OWASP Top 10 for LLMs
The Open Web Application Security Project is a nonprofit organization focused on improving software security. The OWASP Top 10 for LLMs project educates organizations on the potential security risks when deploying and managing LLMs, providing a list of the top 10 most critical vulnerabilities often seen in LLM applications. Reviewing and understanding the list can help developers, designers, architects, managers, and organizations mitigate these risks.
4. Keep a Human in the Loop
AI is smart, but it should only operate partially on its own. Keeping humans in the loop to oversee AI systems is crucial for security, ethical, and validation purposes. Human intervention can help assess the security of AI applications and ensure the protection of sensitive data. They can also evaluate the ethical implications of AI-powered actions and validate AI outputs to reduce the risk of unwanted consequences.
5. Identify and Fix Security Vulnerabilities in Generated Code
AI can greatly speed up development by generating code. However, this generated code can sometimes contain security vulnerabilities. You mean way more often than you'd want. The output of an LLM will only be as good as the input it’s given. You hate to burst the bubble here, but the average quality of open source code isn’t that great, especially regarding security, since OSS is often unpaid/labor of love.
The suggested code generated will also contain software vulnerabilities if training data contains software vulnerabilities. LLMs don’t know they’re suggesting vulnerable code because an LLM doesn’t really understand the context of the code. It doesn’t truly understand the code paths, the data flows, etc.
6. Don’t Give IP or Other Private Info to Public GPT Engines
When using public GPT engines for AI-assisted development, it's essential to avoid giving them any intellectual property (IP) or private information since the public will use the GPT. Sometimes, we might want an LLM to analyze our code, perhaps to understand what it’s doing or to refactor it. Whatever the reason, it’s essential to ensure you’re following the correct IP policies outlined by your organization.
7. Use Hybrid AI Models Where You Can
Hybrid AI models combine different AI techniques and offer better performance and security. Let’s start with the LLM models initially. They’re great for the generative use of AI because they take vast amounts of data in and can reasonably accurately construct a perfect answer as a response, which is understandable.
Does the LLM understand what it just wrote? Does it know the semantics of code or the appropriate pairings in a recipe based on flavor combinations? This is crucial when considering the accuracy or validity of the response it provides.
8. Use Good Training Data
AI models can also exhibit bias based on their training data. It's essential to be aware of this and take steps to reduce bias in your models. Bias in AI refers to systematic and unfair discrimination or favoritism in the decisions and predictions in AI output.
It occurs when these systems produce results that are consistently skewed or inaccurate in a way that reflects unfair prejudice, stereotypes, or disparities, often due to the data used to train the AI or the design of the algorithms. Even reading this blog will bias your views of AI, and you might be thinking of going home and watching The Terminator tonight. Here are some examples of bias:
9. Beware of Hallucinations and Misleading Data
AI models can sometimes produce hallucinations or be misled by incorrect data. The dangers of hallucinations and misleading data as output from an LLM can be very great, and developers should be acutely aware and concerned about these risks.
Hallucinations refer to instances where the AI generates entirely fabricated or inaccurate information. At the same time, misleading data can be more subtle, involving outputs that may appear plausible but are ultimately incorrect or biased.
10. Keep Track of Your AI Supply Chain
Supply chain vulnerabilities are easy to overlook in this Top 10, as you would most commonly think about third-party open-source libraries or frameworks you pull in when you hear the supply chain.
During the training of an LLM, it’s common to use training data from third parties. It’s important to, first of all, have trust in the integrity of the third parties you’re dealing with and attest that you’re getting the right training data that hasn’t been tampered with. OWASP also mentions that LLM plugin extensions can also pose additional risks.
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