A new artificial intelligence (AI) system developed by MIT researchers promises to offer increased threat detection capabilities and reduce false positive rates, boosting incident response and productivity in the security world. The team, based at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL), detailed in the paper AI2: Training a big data machine to defend [PDF], how the new platform achieves three times higher prediction capabilities, and is able to deliver significantly fewer false positive rates than current analytics models. The team showcased the AI2 platform last week at the IEEE International Conference on Big Data Security, and released the study to the public earlier today. The paper explains how the tool combines AI with'analyst intuition' to create a learning model whereby intermittent human analyst feedback is layered into a continuous unsupervised machine learning system. "You can think about the system as a virtual analyst," commented CSAIL research scientist Kalyan Veeramachaneni, who designed AI2 alongside PatternEx chief data scientist and former CSAIL researcher, Ignacio Arnaldo.
Sometimes business analysts who have worked for a long time within an industry, and especially within a single company, have a lot of business process knowledge that explains how certain data came to be coded into the db a certain way -- that can be of tremendous value to a data scientist who is less familiar with the origins of the datasets they use. Enterprise data are rarely so clean and simple to untangle that any newbie can figure out all the linkages that you need to pull together before you have a dataset that you can work with. Furthermore, the origins of the data may hold vital clues as to whether that persistent outlier you see is significant, or if it is just something that came about because someone entered a typo somewhere and it happened to propagate forward unchecked. Those origins can sometimes offer valuable clues when you have to re-engineer a dataset to suit the purposes of your specific modeling task. I personally would be lost without the business analysts who help us out on a daily basis.
As an Insights Analyst supporting our Sales Solutions business, you will shape how we create value for LinkedIn and our customers at scale. In this role, you will collaborate cross-functionally to support, and improve the Insights tools our thousands of sales professionals rely on every day. This includes prototyping, developing, and validating customer-facing solutions that may eventually be integrated into our core enterprise offerings. Using your strong analytic skill and LinkedIn's unique data, you will drive thought leadership and reshape how LinkedIn and our customers sell. Put simply: you will use the LinkedIn dataset to uncover data-driven ways to change the sales industry.
An Analyst works on varied projects with multiple deliverables and varied duties depending on the business objectives. However there are some tasks that can be easily classified as "common everyday duties" in a "typical work day of a business analyst" Analysts like to ask questions. This helps them identify the right question against which they would need to conduct analysis. The process of investigating involves conducting interviews, reading and observing people at work. The analysis phase is the phase during which the Business Analyst explains the elements in detail, affirming clearly and unambiguously what the business needs to do in order solve its issue.