7. AI will take database analysis to a new level
Posted: Sat Feb 08, 2025 8:37 am
Haddad is confident that AI pioneers and leading technology companies will move their developments to Open Source, which will spur a new wave of development in neural networks and deep learning, as happened with Kubernetes - it originated in the depths of Google, but in 2014 the tech giant decided to open the technology. Subsequently, the system was transferred to the management of the Cloud Native Computing Foundation.
"Openness of AI development will allow companies and communities to agree on a number of critical points and begin to create a comprehensive Open Source stack in the field of AI, machine learning and deep learning. Some large companies that have transferred their developments to open source are reaping the first fruits of cooperation with communities," he said.
Data is big data, but it’s growing, which is one of the reasons why CIOs have failed to embrace the role of data analysts. Clearly, as the volume of information grows, it becomes more difficult to find valuable components. “No company in the world has ever managed to hire enough people to keep up with the growth of data. The volume of data is doubling every year, but the budgets and the size of the IT teams are not growing,” Horton said.
Big data analysis and management is another georgia mobile database of AI application where there are high hopes. “In 2019, we will see more companies investing in AI engines to enable their IT teams to process more data in a range of tasks,” he said.
JASK Chief Machine Learning Officer Dean Teffer believes that machine learning has already reached the stage of commercial maturity and that companies will soon begin to use it en masse to work with their databases.
“Enterprise AI 2019 is machine learning that finally moves beyond standard use cases like image labeling or text translation to work on massive amounts of data that has been stored and accumulated for years,” he said, explaining that this is not about a single application of the technology (a one-off analysis or search) but about “solving large-scale data organization and modeling problems that allow business users to collectively connect to AI at any point in time and get reliable data analysis.” He added that improved data visibility will lead to a cascade effect and strengthen the role of enterprise data management in an organization, since it is the data, not the algorithms, that is the repository of valuable information.
"Openness of AI development will allow companies and communities to agree on a number of critical points and begin to create a comprehensive Open Source stack in the field of AI, machine learning and deep learning. Some large companies that have transferred their developments to open source are reaping the first fruits of cooperation with communities," he said.
Data is big data, but it’s growing, which is one of the reasons why CIOs have failed to embrace the role of data analysts. Clearly, as the volume of information grows, it becomes more difficult to find valuable components. “No company in the world has ever managed to hire enough people to keep up with the growth of data. The volume of data is doubling every year, but the budgets and the size of the IT teams are not growing,” Horton said.
Big data analysis and management is another georgia mobile database of AI application where there are high hopes. “In 2019, we will see more companies investing in AI engines to enable their IT teams to process more data in a range of tasks,” he said.
JASK Chief Machine Learning Officer Dean Teffer believes that machine learning has already reached the stage of commercial maturity and that companies will soon begin to use it en masse to work with their databases.
“Enterprise AI 2019 is machine learning that finally moves beyond standard use cases like image labeling or text translation to work on massive amounts of data that has been stored and accumulated for years,” he said, explaining that this is not about a single application of the technology (a one-off analysis or search) but about “solving large-scale data organization and modeling problems that allow business users to collectively connect to AI at any point in time and get reliable data analysis.” He added that improved data visibility will lead to a cascade effect and strengthen the role of enterprise data management in an organization, since it is the data, not the algorithms, that is the repository of valuable information.