Artificial Intelligence has evolved beyond being a machine or programmable device that relies on humans to tell it what to do. The current phase of AI development is self-learning artificial intelligence that can improve itself though user input or the analysis of data created by itself, creating new methods without requiring any user input or assistance. This transformation is causing a total change in the way we think about software and has opened up possibilities we would have never considered to be possible.
Self-learning AI has been created using advanced machine-learning algorithms and in particular, through Reinforcement Learning Algorithms and through Generative Model Algorithms. Reinforcement Learning enables an AI agent to learn through trial and error in that the agent will receive a reward if it performs correctly or will have a penalty if it does not perform correctly. The AI can use millions of different pre-simulated matches with itself to learn how to play very complex games such as Go or Starcraft at a very high level. Similarly, AI can generate an almost endless number of variations from an original dataset such as a large table of musical scores or images, using Generative Model Algorithms to identify the patterns found in large datasets to produce something completely new (e.g., a new musical score or image) without requiring any human intervention.
AutoML is an autonomous machine learning development process allowing AI to configure its architecture automatically through an exploration of various neural network structures to determine the best combination of performance results to produce. This enables AI systems to innovate and evolve at speeds far beyond what human engineers would typically enable them to do, while simultaneously decreasing their dependence upon humans for development.
New innovations that are also being developed include multi-agent AI systems where groups of multiple AI entities are interacting, competing, and collaborating with each other and their environment. These interactions between multiple AI agents can generate “emergent” behavior that their creator (a human) may have been unable to envision prior to the event(s) occurring. For example, AI agents are able to mutually develop their own unique means of communicating with one another and developing problem-solving strategies through the creation of unique “machine cultures” to address the completion of an overall task more effectively/efficiently.
Self-learning AI has been driven mainly by advances in hardware technology. AI systems can use new types of neuromorphic chips that imitate the structure of human brains to develop significantly faster speeds of learning and do so with less energy consumption. By utilizing advanced GPUs or TPUs alongside the new neuromorphic chips, these AI systems are capable of training large-scale models against massive datasets in days rather than many months.
There are many existing examples of how self-learning AI is transforming many fields today. For instance, self-learning AI systems in autonomous cars will progressively learn new information about their surroundings and continuously improve their ability to navigate and safely operate as they are operating. Additionally, self-learning AI systems are being used for drug discovery to analyze millions of compounds and help develop new drugs many times faster than a typical researcher. Furthermore, self-learning AI systems are also operating in the financial industry by learning about subtle patterns in the marketplace and helping managers dynamically optimize their investment portfolios.
However, the new technology of self-learning AI also presents serious challenges. Specifically, self-learning AI may behave in ways that were unexpected as a result of discovering solutions through experimentation that are beyond the immediate understanding of a human. Therefore, ensuring that these systems are robust, safe, and explainable will ultimately be necessary for self-learning AI systems to operate as intended in the real world. To address these issues, researchers are working to develop new techniques to mitigate the risks including the use of model interpretability tools and simulation testing.
The creation of self-learning AIs is transforming computing technology. Self-learning AIs instead of only executing commands, are developing their own experimental processes, adapting to their environment, and creating new possibilities. This shows that self-learning AIs represent a new period of intelligent machines that can facilitate the discovery of science, automate complex processes, and solve large-scale problems that have exceeded the capabilities of human capabilities.
Ultimately, AI has transitioned from fixed programmed assistance to powerful self-learning innovation through this evolution of computers. By teaching themselves, AIs have moved from programmers of technology to guiding and directing the knowledge gained through this technology. The future will bring many new abilities and technologies as AIs develop and improve their ability to self-learn beyond the limits of our current understandings. Overall, self-learning AI is already having a profound effect on almost every aspect of technology and everyday life and it is anticipated that this trend will continue to grow in the future.
