AI technologies are being used to reduce credit card fraud, combat fake reviews, and increase approval rates for loans. It is also being used to protect military assets through sUAS threat detection and more.
Achieving theory-level AI, self awareness, is the next step in this technology’s evolution. Researchers and engineers are attempting to develop rudimentary versions of this technology.
1. Deep Learning
Deep learning is a form of machine learning that uses artificial neural networks to mimic how the brain learns. This allows it to process large amounts of data and extract patterns and trends that might otherwise be missed by human analysts.
The most common applications for this type of AI are in computer vision, conversational AI, and recommendation systems. In these, algorithms are able to recognize images and videos, understand natural language, and leverage past behaviors and preferences to offer personalized recommendations.
Unlike standard machine learning models, deep learning algorithms use a structure called neural networks, which are layered and resemble the neurons in the brain. They receive raw input, process it through the hidden layers, and develop a final output. This approach is more sophisticated than the simple logic-based calculations used in standard machine learning models.
For example, if a machine learning model wanted to determine whether an image contains a car, we humans would first have to identify unique features of cars (shape, size, windows, etc.) and extract them as features before the algorithm could make a determination. In contrast, deep learning models like CNNs (convolutional neural networks) can recognize a car in an image without the need for any feature extraction.
2. Natural Language Processing
Natural Language Processing (NLP) is a subset of artificial intelligence that allows computers to understand and manipulate human language. It’s the technology behind virtual assistants like Siri, Cortana and Alexa as well as chatbots and search engines like Google. NLP enables machines to interrogate data using natural language text and voice.
Its applications are widespread and diverse, from customer service to analyzing medical images and providing personalized learning experiences. NLP can also automate repetitive tasks, allowing humans to focus on more complex projects.
AI-powered NLP tools are also being used to develop augmented reality and chatbots that can understand context, provide recommendations and make suggestions. It is also being used to create targeted advertising, ensuring that the right message reaches the most relevant audience.
NLP can be used to analyze text and speech to determine meaning, identify words and phrases, classify entities, and perform translations between languages. It can also be used to generate new text, such as summaries of findings from a business intelligence platform or news articles. It can also be used to enable smarter search, enabling a chatbot to understand a user’s request and return results that match the query, much as you would ask Siri for information.
3. Machine Learning
ML models “trained” on massive datasets can gain insight and automate decision-making where human ability or speed is limited. Machine learning is used in applications such as image recognition, text analysis, and e-commerce analytics.
For example, Clarifai uses ML to categorize images and videos, then searches its database for similar content. It also performs tagging, allowing users to search for specific words or phrases. The company offers solutions through mobile, on-premises, or API interfaces.
AI-powered document processing companies, like HyperScience, use ML to cut down on manual data entry work and automate the process of extracting relevant information from handwritten forms. It also processes information in PDFs, eliminating the need for a human to read through entire documents.
Generative AI, such as ChatGPT and Midjourney, have drawn the most attention among consumers exploring generative AI, but smaller, more narrow-purpose tools may have more staying power in business, particularly for enterprise apps with factual knowledge or specialized user needs. In 2024, the Jupyter Notebook open source platform is making a big push to provide a unified environment for AI development with a code-first approach. It’s used by many data scientists in the industry.
Many people have come across chatbots when they use a company’s live chat feature on its website. These automated agents work to respond to customers’ questions, and are designed to be both conversational and easy to use. These AI technologies are used to resolve issues, free up phone lines, provide 24/7 customer support and are far less expensive than hiring human employees to do the same job. Here is a great tutorial to help you start using ChatGPT AI for your businesses.
Among the most popular types of chatbots are rules-based, which operate based on simple keyword detection. These chatbots have been around for decades and are similar to the Interactive Voice Response (IVR) systems that people call into to order products or find customer service representatives.
A more advanced chatbot is one that uses artificial intelligence to understand context and meaning in what it hears from a user, and then respond accordingly. This type of AI-powered bot is also called a conversational chatbot, and is more effective than rule-based models that can only give pre-programmed answers. In this case, the chatbot might draw on previous conversations, documentation from other chatbots, and databases to help determine the best way to help a customer. These AI chatbots are also more complex to develop, but they can be more responsive and accurate than rule-based chatbots.
5. Cognitive Computing
Cognitive Computing is an advanced form of AI that uses pattern recognition, data interpretation and context-aware decision making. The most well-known example of this is IBM Watson, which is used by healthcare providers to help with medical treatments and other business processes.
This type of AI uses a broad range of technologies such as natural language processing, NLP and image and video recognition. It also has the ability to understand and interpret unstructured data, which makes it a valuable tool for businesses dealing with a lot of information.
In addition, it can also recognize and interpret emotions in speech and facial expressions, which can be a useful tool for companies looking to reduce credit card fraud or stop fake reviews online. This technology can also be used to personalize learning materials for students based on their individual pace, which is especially helpful in education.
However, if the theory of mind aspect of Cognitive Computing becomes more advanced, it could create some risky situations, such as robots becoming self-aware or feeling emotions and reacting to them in ways that are not predictable by humans. Therefore, the emergence of this technology will require companies to develop governance frameworks that balance supporting innovation with protecting privacy and security.
6. Robotic Process Automation
Robotic process automation, or RPA, uses software bots to automate partially or fully manual operations that are repetitive and rule-based. This technology enables workers to focus on higher-level work. It is a key component of AI that is driving new efficiencies and freeing people from mundane, repetitive tasks across a wide range of industries and business processes.
RPA is also used in customer service, compliance and legal, IT, supply chain, marketing and other departments. For example, companies use RPA to reduce credit card fraud by using the voice recognition capabilities of artificial intelligence to listen for a specific pattern and identify potential fraud.
Similarly, security providers use it to detect cyberattacks in real-time. The UK based cybersecurity firm Darktrace uses self-learning AI to protect customers from sophisticated cyberattacks by analyzing behavior patterns, detecting anomalies and predicting attacks before they happen.
Aside from these uses, this technology is also being leveraged in the education sector to enhance learning and increase employee productivity. For instance, AI is transforming learning management systems to provide a wider array of analytics and insights. These analytics help organizations improve course content, evaluate employee performance and make crucial decisions based on data.
7. Artificial General Intelligence
Artificial general intelligence refers to the hypothetical future state of AI that is equal to or surpasses human intelligence. It would enable systems to perform tasks across a range of fields, adapt to changing environments and solve new problems—rather than just the ones that were included in their training data.
The high-profile success of Google DeepMind’s AlphaGo machine, which beat a world champion at the notoriously complex game of Go, can give the impression that we are close to reaching AGI. However, most of the systems in use today are rather narrow AI: They excel at a single task after rigorous training but can’t do anything else.
Building large language models and other powerful generative AI technologies requires tremendous amounts of compute and data. Open source, a model whereby code is made publicly available for free, can reduce costs and extend access to these technologies. According to GitHub’s 2023 AI Developer Report, generative AI projects have climbed into the top 10 most popular on the code hosting platform.
Marketing is another area where AI has become widely used, particularly for delivering highly targeted, personalized ads. AAI tools help marketers analyze vast amounts of raw, unlabeled data to identify and target specific audiences with relevant content.