Rapid Progress In Artificial Intelligence
Innovation in the field of artificial intelligence (AI) is rapid. Developments that were new and exciting 10 years ago such as deep learning are now the status quo when it comes to best practices. At the intersection of science and technology, AI practitioners are determined to make progress and change the way society works.
Just a few years from now, modern AI will look different. Some methods used today will be obsolete and new ideas will redefine the field. Research that is in nascent stages today will come to define the art of the possible for tomorrow.
Future Developments In AI
1. Unsupervised Learning
These days, the current paradigm in AI is supervised learning. Supervised learning involves human “supervisors” curating data sets with predefined categories for AI models. The human element of supervised learning is expensive and time consuming. Manually labeling data points creates a bottleneck in the process. It also places predefined limitations on AI on concepts and categories. AI can only operate on the given information and relationships in a data set given by supervised algorithms. The future of AI includes unsupervised learning which removes human guidance from the process. The premise of unsupervised learning is that it would operate much like human cognition does by exploring the world. A system forms an understanding of the environment from observing patterns and relationships in parts of the environment. Unsupervised learning is “learning everything from everything else” and it already has had transformative results in Natural Language Processing (NLP) and holds the key to developing human-level AI.
2. Federated Learning
Data is the currency of AI. This makes data privacy an integral issue in the successful execution of AI models. Data privacy is regulated by various laws and regulations. Therefore, the practice of federated learning is focused on enabling AI models to learn from a diverse set of datasets without compromising privacy. For modern AI models, the traditional practice is to gather datasets in one place such as the cloud to train the model on the data. This is nota practical or efficient approach in today’s data environment which has security and privacy regulations about creating a central data repository. Federated learning was first studied by researchers at Google and has gained significant interest in the field. In application, federated learning has made tremendous gains in fields like healthcare. Healthcare is a highly regulated industry with laws like HIPAA guarding sensitive patient information. The use of federated learning has enabled AI practitioners to develop lifesaving AI tools without privacy breaches.
3. Nuance In AI
In the last decade, we have witnessed the golden age of AI. In the arena of work, AI is defining the future of what is possible and what can be imagined when it comes to automation. NLP is also having a moment with advancements in parallelization. Parallelization occurs when all tokens in a sequence of text are analyzed at once rather than sequentially. This improves the accuracy and speed of AI models. Furthermore, even the US military has made advancements in AI by developing algorithms which can detect sarcasm in text. The military uses this capability to analyze the intent and content of social media posts in countries of interest. A new wave of AI capabilities is being worked on including ambiguities, colloquialism, and the evolution of language.
The future of AI is here, and it looks promising for advancements in supervised learning, federated learning, and nuance of language in NLP practices.