The Evolution of AI: Transforming Industries and Shaping the Future

How machine learning, deep learning, and generative AI are revolutionizing industries and offering new opportunities for businesses to thrive.

Artificial intelligence (AI) has come a long way since its inception in the 1950s. From the first working AI programs to the establishment of the MIT AI Lab, AI has evolved to include various approaches such as machine learning (ML), deep learning, and generative AI. These advancements have revolutionized industries, transforming the way businesses operate and opening up new opportunities for innovation.

Machine Learning: The Backbone of AI Applications

Machine learning is a subfield of AI that uses algorithms to analyze data and make predictions. It is responsible for innovations such as speech recognition and search engines, which are now embedded in everyday life. There are three main types of machine learning approaches:

1. Supervised learning models use previous data to classify or make predictions with new data. The previous data have features with target values for a model to assess itself against. The model acts like a student, continuously retaking a test, trying to assess how good he or she is at interpreting questions and answering them. Examples of supervised learning algorithms include linear regression, logistic regression, support vector machines, random forests, and k-nearest neighbors.
2. Unsupervised learning models do not have target values – instead, these models organize data into groups for analysis. A common use of unsupervised learning is customer profiling. For example, algorithms scan social media for likes, pictures, and biographies to identify common characteristics from which consumer segments can be formed. Examples of unsupervised algorithms include k-means clustering, hierarchical clustering, DBSCAN, and PCA.
3. Reinforcement learning models use trial and error to continuously train themselves from data in the environment to make the best possible decisions. For example, a driverless car trains itself with road simulations and test drives in controlled environments to gather data and make the most appropriate decisions on the road. Examples of reinforcement learning algorithms include q-learning, SARSA, and DQN.

Deep Learning: Unleashing the Power of Neural Networks

Deep Learning is a subset of machine learning that uses neural networks, inspired by the human brain, to make decisions through hidden layers that can find patterns from data. If a neural network contains multiple hidden layers, it is said to be “deep”. However, this definition is constantly evolving. The depth of these networks enables them to learn complex, hierarchical representations of the input data. Deep learning has gained significant attention in recent years due to its ability to automatically extract meaningful features from raw data, leading to breakthroughs in various domains such as image recognition, natural language processing, speech recognition, and more. Some popular models include convolutional neural networks, recurrent neural networks, and transformer models.

Generative AI: The Next Frontier

Generative AI refers to a class of artificial intelligence models and algorithms that focus on generating new data samples, often mimicking the properties of a given dataset. These models learn the underlying patterns, structures, and probability distributions of the input data and can create new, previously unseen samples that share similar characteristics with the training data. Generative AI has been used for various applications, including image synthesis, text generation, music composition, data augmentation, and more.

Generative AI differs from other AI models or approaches, such as discriminative models, in terms of the learning objectives and problem-solving strategies. While discriminative models focus on learning the decision boundary between different classes or predicting target values given input features, generative models aim to model the process of generating the data itself.

Some popular generative AI models include:

1. Generative Adversarial Networks (GANs): GANs consist of two neural networks, a generator and a discriminator, trained simultaneously in a competitive fashion. The generator creates synthetic data, while the discriminator evaluates the quality of the generated data, trying to distinguish between real and fake samples. The training process refines both networks, leading to the generator producing increasingly realistic data.
2. Variational Autoencoders (VAEs): VAEs are a type of unsupervised generative model that learns to encode input data into a lower-dimensional latent space and then decode it back to the original data. They impose a probabilistic structure on the latent space, enabling them to generate new data samples by sampling from the learned latent distribution.
3. Autoregressive models: These models generate new samples by predicting one data element at a time, conditioned on the previously generated elements. Examples include PixelCNN for image generation and transformer-based language models like GPT-3 for text generation.
4. Restricted Boltzmann Machines (RBMs) and Deep Belief Networks (DBNs): These are types of probabilistic graphical models that learn a joint probability distribution over the input data and can generate new samples by sampling from the learned distribution.

The primary distinction between generative AI and other AI models is that generative AI focuses on creating new data samples, while other models typically concentrate on tasks such as classification, regression, or prediction.

Players in Generative AI

1. OpenAI : OpenAI is a leading AI research organization that has developed various generative models, including GPT-2, GPT-3, and Codex, which have shown impressive capabilities in natural language generation and understanding.
2. Cohere Technologies: Cohere is an AI startup that develops advanced natural language understanding models, enabling applications like chatbots, search, and content generation.
3. 谷歌 Brain: Google Brain is another AI research team within Google, and they have contributed to the development of generative models, such as the Transformer architecture, which powers BERT and T5, both widely used in natural language processing tasks.
4. Meta AI Research (FAIR): FAIR is Facebook's AI research division, which has developed generative models like Seq2Seq and MaskGAN. They also contribute to the development of AI tools, such as PyTorch, which can be used for building generative models.
5. 英伟达 : NVIDIA, a company primarily known for its graphics processing units (GPUs), has also ventured into AI research, developing generative models like StyleGAN and StyleGAN2, which are capable of generating highly realistic images.
6. EleutherAI : EleutherAI is an independent research organization that has developed GPT-Neo, an open-source alternative to GPT-3, and GPT-NeoX, their next-generation language model project.

Enabling the Enterprise - Do Not Fear AI, Embrace it

The use cases are endless, and there is so much innovation happening already. Enterprises need to dedicate budgets and resources conceptualize how to use the technology to transform their business and build experiences that not only impact the bottom line, but enable its workforce and enhance its services.

Banking is one vertical that we can look at. The use cases for AI are unlimited when looking at the entire value chain of a bank. However, let's look at one example where it can be quite impactful.

Debt collection is a big ticket item for banks. With hundreds of thousands of customers in the collections process on any given day, banks must consider where and how to allocate resources, such as collections agents, most efficiently. Banks typically employ thousands of people both directly and through third party contractors who work on recovering outstanding debt. For the Retail and Small Business Credit Risk department, optimizing collections “triage” based on customer risk levels and treatability is a significant task.

Optimizing collections has a direct positive impact on the Bank’s Income Statement and Balance Sheet. Collections go beyond reacting against failures to pay; the Bank has an opportunity to build goodwill with customers and assuage detrimental actions to protect their credit, leading to more customer satisfaction in the long-run.

Typically agents use models to determine which customers to call first based on risk of charge-off. Traditionally, most risk models use a logistic regression model that factors in 20-30 variables. However, leveraging deep learning, new models can be built factoring in over 300 variables are leveraged to provide the most accurate ranking. Combine this with a friendly user interface like chatGPT that agents can interact with and now you have a powerful tool. This would not only improve the productivity of agents exponentially, but it would also result in millions in cost savings.

Conclusion

The evolution of AI and its various approaches, such as machine learning, deep learning, and generative AI, have revolutionized numerous industries and transformed the way businesses operate. As AI continues to advance, enterprises must embrace this technology to stay competitive, optimize their processes, and enhance customer experiences. The banking sector serves as a prime example of how AI-driven innovations like deep learning and generative AI can optimize operations and deliver significant cost savings, while also improving customer satisfaction. By harnessing the power of AI, businesses can not only streamline their operations, but also redefine the future of their industries.