That does not compute.

This week, scientists from around the world have been discussing the challenges they face in their research, specifically the issue of inaccurate data. In a conference titled "Machine Learning Unbound," renowned AI researcher and data scientist Dr. James Walters presented his concerns about the reliability of the data being used in the development of new technologies.

Walters claims that a significant percentage of the data being used to feed machine learning algorithms is incomplete or incorrectly formatted. This has led to a situation where the system produces results that do not make logical sense, hence the phrase "That does not compute."

During his presentation, he went into detail about some of the ways that this is happening. For example, he pointed to the growing trend of data vendors providing data from unverified sources to appeal to the needs of customers who are seeking to collect as large a dataset as possible. "The end goal has become to simply amass as much data as possible, without considering the integrity of what we're gathering," Walters said.

In response, some researchers have proposed solutions that involve creating better checks for data integrity, which could come in the form of using more advanced cleaning methods or implementing stricter sourcing protocols. There is also talk about the need for educators to do a better job of teaching students about the importance of data accuracy and the consequences of ignoring its integrity.

The conference also addressed the role that machine learning algorithms have in changing the nature of research. Dr. Walters argued that the process is being quickened by the algorithms and their ability to quickly and efficiently analyze data. However, he warned that this speed can lead to rushed conclusions and a disregard for the need to verify the data.

In the end, the conference resulted in an agreement among many of the scientists that the time has come for a new standard of data integrity. They acknowledged that it will require a change in our approach to producing and using data and that doing so will be essential to the progress of AI technology and other fields that rely on accurate data.