Artificial Intelligence (AI) is revolutionizing the way we interact with technology, and it has the potential to transform society. As AI becomes even more sophisticated, we can expect to see many new exciting applications in various fields. From more advanced chatbots and virtual assistants to robots that can learn and adapt to complex environments, here are 5 AI technologies to watch over the next 24 months:
Chat GPT 4
OpenAI’s GPT-3 (Generative Pre-trained Transformer 3) is an advanced natural language processing model that can generate contextually relevant text. GPT-3 has a wide range of applications, from language translation and content creation to chatbots and virtual assistants. As AI continues to advance, GPT-3 and other natural language processing models are likely to become even more sophisticated, enabling more natural and human-like interactions between humans and machines.
Recently, I asked ChatGPT 3 if it could help me write a science-fiction book. It responded with some helpful information but gave me the following disclaimer.
“Keep in mind that while I can generate text based on your prompts, I am not a substitute for the creative process of writing a book. Writing a book takes time and effort, and ultimately the ideas and words you use to create your story must come from you. Nonetheless, I am here to assist you with the process and help you achieve your goal of writing a great book.”
GPT-4 is slated to be more creative and collaborative than ever before. It will generate, edit, and review content with users on creative and technical writing tasks, such as composing songs, writing screenplays, or learning a user’s writing style. This means that ChatGPT could play a larger role in the creative process of writing a book. It could help me develop the plot, characters, and other important elements of the story.
Furthermore, OpenAI’s internal evaluations claim that GPT-4 is 82% less likely to respond to requests for disallowed content and 40% more likely to produce factual responses than GPT-3.5.
Generative Adversarial Networks (GANs)
Many years ago, I started using flight simulator programs so that I could learn the basics of flying. Early versions of flight simulators had unrealistic images, such as square shaped clouds and terrain. Over the years, integration with satellite images and local weather combined with faster processing power on local computers has allowed flight simulators to feel much more realistic. However, there are still many gaps to making an application feel as real as the actual experience.
Imagine a video game where the player enters a virtual world that is a perfect replica of a real-world city. The streets, buildings, and people are all rendered with incredible detail and accuracy, making the virtual world feel almost like the real thing.
Generative Adversarial Networks (GANs) are a type of artificial neural network that are used for generating new data that is similar to a given dataset. GANs consist of two neural networks: a generator network and a discriminator network. The generator network creates new data that is like the original dataset, while the discriminator network evaluates the similarity between the generated data and the original dataset. The two networks are trained together in an adversarial process, where the generator network learns to create data that can fool the discriminator network, while the discriminator network learns to distinguish between the generated data and the original dataset.
This adversarial training process offers several benefits over traditional generative models. By training a generator and a discriminator network together, more realistic results can be generated, making it difficult to distinguish the data from real data. This happens because the generator network is constantly trying to create data that can fool the discriminator network, forcing it to learn how to create data that is increasingly realistic and similar to the original dataset.
Advanced GANs technology has the potential to revolutionize the way we create and interact with virtual environments, making them more realistic than ever before. Soon, GANs technology could be used to create realistic virtual humans that are almost indistinguishable from real people. Realistic humans could be used in a range of applications, such as virtual reality training simulations, entertainment, and marketing. Organizations could one day use a virtual human in a marketing campaign to promote a new product or service.
Reinforcement Learning (RL)
One day, humans could have personal assistants in the form of robots. These robots could be used to play games with the kids or to help with the yard work. Robots could also be used for commercial or government initiatives. For example, they could be used to navigate through environments that are too dangerous for humans, such as deep-sea exploration, space exploration, or disaster response scenarios. Robots could also learn to adapt to the unique challenges of an environment and to operate independently for extended periods of time.
Reinforcement Learning (RL) is a type of machine learning that involves training an agent to interact with an environment to achieve a specific goal. In RL, the agent receives feedback in the form of rewards or punishments and uses this feedback to learn how to navigate the environment and to make decisions that lead to a desired outcome. RL machine learning could enable robots to make decisions in a dynamic environment through trial and error. This means that robots could be trained to perform a range of tasks, from simple tasks like grasping and manipulation to more complex tasks that involve navigating through a space, interacting with humans, or collaborating with other robots.
RL learning could be used to develop drones for search and rescue operations. In search and rescue missions, drones are often used to locate and assist people in remote or hazardous environments. With RL technology, drones would be able to learn how to navigate these environments and adapt their behavior to different situations. For example, drones could learn how to work together while they learn how to detect and avoid obstacles. They could learn how to navigate through changing weather conditions, and how to locate people who may be difficult to find.
AI-powered chatbots, such as ChatGPT, have the potential to revolutionize the way call centers provide IT support and customer service, providing an intelligent and conversational interface that can handle multiple inquiries at the same time. This would likely reduce call wait times and improve the customer experience.
Imagine a customer who is having difficulty finding the information they need on a company’s website. With an AI-powered chatbot, the customer could simply ask a natural language question and receive a clear and concise response in real-time.
Although customer service management solutions could incorporate some similar features to ChatGPT, it is important to understand that ChatGPT is a highly advanced AI model that has been developed and trained over many years. Creating a similar solution would require significant time, effort, and expertise in NLP, machine learning, and AI software development.
It is technically possible for a customer service management (CSM) or an IT service management (ITSM) company to license technology like ChatGPT, however careful evaluation and consideration of factors such as data privacy and security need to be considered.
Federated Learning is an approach to machine learning that enables the training of models on decentralized data sources without the need for centralized data storage. In traditional machine learning approaches, data is typically stored in a centralized location, such as a server, which is used to train the model. However, in Federated Learning, data is distributed across multiple devices or servers, and the model is trained locally on each device or server.
This approach has several advantages. It allows the privacy of data to be maintained while still training models effectively. Since data remains on the devices where it was generated, there is no need to transfer it to a central location for training, which reduces the risk of data breaches and privacy violations. Moreover, the data remains under the control of the users or organizations who generated it, which increases trust and participation in the machine learning process.
Federated learning could enable customer service management and IT service management solutions the ability to license advanced chatbots or virtual assistants in their solutions. These chatbots could be tailored to their customer’s needs and preferences, while still maintaining the privacy and security of user data.
Explainable AI (XAI)
Responsible AI is an approach to developing and deploying AI technologies that is ethical, transparent, and accountable. It aims to ensure that AI systems are developed and used in a way that is safe, fair, and beneficial to society as a whole. The goal of responsible AI is to ensure that AI systems are developed and used in a way that aligns with societal values and goals, and that they contribute to the betterment of society as a whole.
Explainable AI (XAI) is an approach to developing AI systems that are transparent and can be easily explained to users and stakeholders. The goal of XAI is to increase trust in AI systems by allowing users to understand how the system works, how it arrived at a particular decision, and why it is making a certain recommendation.
As AI becomes more sophisticated, it will be increasingly difficult for humans to understand how AI systems are making decisions. Explainable AI (XAI) is an emerging field of AI research that seeks to make AI systems more transparent and understandable. XAI will be important for building trust in AI systems, and for ensuring that AI is used ethically and responsibly.