Case-Based Reasoning relies on the underlying hypothesis that similar problems have similar solutions. The extent to which this hypothesis holds good in the case base has been used by CBR designers as a measure of case base complexity, which in turn gives insights on the generalization ability of the reasoner. Several local and global complexity measures have been proposed in the literature. However, the existing measures rely only on the similarity knowledge to compute complexity. We propose a new complexity measure called Reachability-Based Complexity Measure (RBCM) that goes beyond the similarity knowledge to include the effects of all knowledge containers in the reasoner. The proposed measure is evaluated on several real-world datasets and results suggest that RBCM corroborates well with the generalization accuracy of the reasoner.

AI or artificial intelligence use and applications are growing at an accelerated rate! Competitors like Russia and China will soon pass us in artificial intelligence proficiency if we do not continue to advance. For example, Russia has made weaponizing AI for military purposes a priority! Artificial intelligence investments have been boosted by the need for enterprise digital transformation during the pandemic. Last year, AI startups raised a collective $73.4 billion in Q4 2020, a $15 billion year-over-year increase.

All the sessions from Transform 2021 are available on-demand now. The need for enterprise digital transformation during the pandemic has bolstered investments in AI. Last year, AI startups raised a collective $73.4 billion in Q4 2020, a $15 billion year-over-year increase. And according to a new survey from ManageEngine, the IT division of Zoho, business deployment of AI is on the rise. In the survey of more than 1,200 tech execs at organizations looking at the use of AI and analytics, 80% of respondents in the U.S. said that they'd accelerated their AI adoption over the past two years.

Intelligent algorithms are recently used in the optimization process in chemical engineering and application of multiphase flows such as bubbling flow. This overview of modeling can be a great replacement with complex numerical methods or very time-consuming and disruptive measurement experimental process. In this study, we develop the adaptive network-based fuzzy inference system (ANFIS) method for mapping inputs and outputs together and understand the behavior of the fluid flow from other output parameters of the bubble column reactor. Neural cells can fully learn the process in their memory and after the training stage, the fuzzy structure predicts the multiphase flow data. Four inputs such as x coordinate, y coordinate, z coordinate, and air superficial velocity and one output such as pressure gradient are considered in the learning process of the ANFIS method. During the learning process, the different number of the membership function, type of membership functions and the number of inputs are examined to achieve the intelligent algorithm with high accuracy. The results show that as the number of inputs increases the accuracy of the ANFIS method rises up to R^2>0.99 almost for all cases, while the increment in the number of rules has a effect on the intelligence of artificial algorithm. This finding shows that the density of neural objects or higher input parameters enables the moded for better understanding. We also proposed a new evaluation of data in the bubble column reactor by mapping inputs and outputs and shuffle all parameters together to understand the behaviour of the multiphase flow as a function of either inputs or outputs. This new process of mapping inputs and outputs data provides a framework to fully understand the flow in the fluid domain in a short time of fuzzy structure calculation.

That's where Machinify comes in. The artificial intelligence company just raised a $10 million Series A round led by Battery Ventures with participation from GV and Matrix Partners. "Our core notion is that today, enterprises are collecting a ton of data," Machinify founder and CEO Prasanna Ganesan told TechCrunch. "But if you look at how many of them are successful in turning it into smarter decision-making to drive efficiency, very few companies are succeeding." With Machinify, enterprise customers feed the system raw data, specify what they're trying to optimize for -- whether that be revenue or some other goal -- and then the machine figures out what to do from there.