The Deployment and Demystification of AI
In 2019, society will push the providers of AI solutions to be more transparent about the intentions of their technology and how they discover insights and process data. The general buzz of implementing AI for the sake of it will wear off and companies will demand that AI clearly demonstrate its positive impact on their business. As people awaken to AI not as something designed to replace, but rather augment their natural abilities, we will see AI be more widely adopted in the workplace. Find out more about what’s to come in the AI field from John Kane, Distinguished Scientist, Machine Learning at Cogito.
A Change in Terminology
In 2019, as an industry, we need to move away from the over-reliance on catch-all, ambiguous terms like “AI,” and develop terms the provide more insight and clarification into the type of technology being developed and deployed. With an increase in general sophistication in machine learning, new terminology is beginning to arise to refer to more specific and nuanced technology areas. This has already begun with terms like Augmented Intelligence to refer to machine learning-based technologies which are specifically developed to help and enhance human activity rather than replace it. The industry as a whole needs to help classify and categorize AI, so that there is a clear understanding of what machine learning is and how organizations can glean the insights they’re receiving to help drive results.
Machine Learning Models Will Become More Context-Aware
In 2019, context awareness will become even more important for machine learning. Machine learning models will need to know more about context (e.g., what device is being used, what prior information is known about the user) and adapt accordingly. There will be increased interest in understanding the emotional and health state of users of these technologies, along with the need for contextually appropriate responses in the year ahead.
Democratization of Machine Learning
Despite “democratization of machine learning” solutions and increased research attention on automated machine learning, for instance Google’s recent AdaNet autoML system, there will continue to be a growth in demand for machine learning and deep learning specialists in the year ahead. For modeling problems which are considered to be “solved” these automated modeling systems will receive increased adoption, but for new modeling problems or for areas which are still very much “unsolved,” there will be an increasing need for skilled scientists and engineers. In 2019, the industry (and academic institutions) should focus on developing resources such as courses on ML and AI and continue to invest in addressing the current skills gap issue and keep up with the ever-increasing labor demand.
Discrimination and Bias in ML/AI
Businesses and society in general are becoming increasingly concerned about discrimination and bias introduced by machine learning and AI. This is with good reason, as there have been several cases reported including credit rating algorithms treating people from certain demographics unfairly and image processing models incorrectly classifying the color of people’s skin. As a result, there is an onus on technology companies to implement processes which mitigate bias in their AI systems in 2019.
As we head into 2019, companies should adopt and implement a model development protocol, which follows the FAIR framework. This involves using data collection and machine learning techniques to reduce the effect of bias in models. If companies fail to implement this, they will receive negative responses from the public and their target customers and have ineffective applications.