MIT Tech Review article goes deeper into issues on how a closed system is a “double-edged sword.” Good part of closed system is that most of hackers are kept out and thus provide a security to general users Bad part of closed system is that very advanced hacker are able to break in and hide with out being discovered. Closed system also mean that it is extremely difficult to…
What challenges are ML Engineering team facing? Key success factor that defines a ML Engineering team is that they have access to Data and then they have automated their software engineering to achieve agility and efficiency in delivering value to the business. A ML Engineering team faces following challenges in two broad category. DataOps – Data Engineering Data collection from different sources and extraction Data pipelines that primes for…
Is DevOps a necessity? Under Engineering Practice, software engineers are empowered to have responsibility developing high quality products through use of test driven development and other engineering practices that enable them to build features in agile fashion. Engineering teams are enabled to rapidly iterate through features. These features may be empirically tested with customers such that only customer impacting features are productized. Such a process will improve performance to…
ML Engineering Developing and releasing quality software is hard. If there is no automated process, it is downright expensive and increases risk to business due to unseen failures. For a software engineer, being able to write, test, and quickly deploy and perform tests (integration, performance, vulnerability) are very important to have a successful non-eventful release. An Organization must develop an engineering cultural practices that recognizes continuous – building, testing,…
Putting Data Science in Perspective Data Science projects work may be broken into two major parts. They are Data Engineering and Data Science. Data Engineering is about Sourcing the data from different databases inside your enterprise or outside your enterprise Doing ETL that includes cleaning/cleansing of data. At this point you may also sanitize/anonymize such that data is secured while working on Data Science Doing feature engineering to extract…
As AI and ML is getting pervasive into our technology landscape, there is a huge benefit in getting your Data Scientists to focus on models rather than working on Data Engineering or worse wait on Data Engineers. There are many different acronyms – DataOps, Data Engineering, Feature Engineering, and even MLOps to indicate improvement in process and engineering. For building and deploying ML to production at scale, a key…
New research reveals biases in fake news datasets and improves the use of automatic detectors. MIT Newshttp://news.mit.edu/2019/better-fact-checking-fake-news-1017 To prove this, the researchers developed attacks that they showed could fool state-of-the-art fake-news detectors. Since the detector thinks that the human-written text is real, the attacker cleverly (and automatically) impersonates such text. In addition, because the detector thinks the machine-generated text is fake, it might be forced to also falsely condemn…
Making sense of raw point-cloud data is difficult, and before the age of machine learning it traditionally required highly trained engineers to tediously specify which qualities they wanted to capture by hand. But in a new series of papers out of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), researchers show that they can use deep learning to automatically process point clouds for a wide range of 3D-imaging applications. MIT Newshttp://news.mit.edu/2019/deep-learning-point-clouds-1021
http://news.mit.edu/2019/helping-autonomous-vehicles-see-around-corners-1028 In a paper being presented at next week’s International Conference on Intelligent Robots and Systems (IROS), the researchers describe successful experiments with an autonomous car driving around a parking garage and an autonomous wheelchair navigating hallways. When sensing and stopping for an approaching vehicle, the car-based system beats traditional LiDAR — which can only detect visible objects — by more than half a second. “MIT researchers have developed…
A new computer model that uses machine learning and de-identified and aggregated search and location data from logged-in Google users was significantly more accurate in identifying potentially unsafe restaurants when compared with existing methods of consumer complaints and routine inspections, according to new research led by Google and Harvard T.H. Chan School of Public Health. The findings indicate that the model can help identify lapses in food safety in…
