Advances have been enabled by deep learning techniques that can now assess the patterns or word usage and structure from large text. The growing interest of scientists in deep learning has boosted the capabilities of computer speech recognition. Through advancement in deep learning algorithms, error rates can be lowered compared to those made by professional transcriptionists, although intense training of these systems is still required to attain that fully.
Expanding uses
Although the main aim may be academia for now, AI can go above and beyond to other areas. For instance, AI can be useful for consumers who want to adopt it for different purposes. An example of other uses of AI can be in chatbots and voice assistants, both of which are created based on state-of-the-art language models. Chatbots and voice assistants are now used by many institutions in different sectors such finance, healthcare and even government agencies. With a lack of understanding of language, these systems may become ineffective and may slow access to critical services in the industries listed above.
The Winograd Schema Challenge of 2011 was a turning point for AI in relation to natural language processing. The challenge used 273 sets of questions that has pairs of sentences that are the same except for one word that is called a trigger. The NLP system is required to find the difference as a solution to the problem. Such undertakings have seen machine learning and AI coming closer and closer in capabilities to understanding humans, and researchers are predicting that it will not be long before they surpass humans.
Better all the time
For AI to match the capacity of humans, a lot of learning is required. This is what the researchers are currently doing as they are now carefully collecting data from thousands of languages to be used to teach speech recognition systems. Other developments have ushered in a new era of AI where this technology can now perform much more complex tasks that will not depend on pre-programmed rules to make decisions.
For instance, Robo-advisors such as Betterment among others, are gaining popularity in the finance industry as they can control funds without human input. Professionals in this field believe that AI systems within a few years will become more sophisticated to the extent that no humans will be able to beat them. The question that some people ask is how. Well, with deep learning that takes the concept of AI to the next level by modeling complex non-linear relationships of different layers, a computer can learn from observing data. It uses deep neural networks that is a biologically inspired paradigm that gives the computer the ability to learn like humans.
While it is clear that artificial intelligence has made significant improvements from what it was in the past, a lot needs to be done if it is to be good than humans or even be at the same level. One thing that is for sure is that the road to this success cannot be paved with one discipline. Rather, it requires a collection of techniques, theories and inputs that allow AI to evolve fully.
