Migrations of systems from on-site premises to the cloud has been a fundamental endeavor by many industrial institutions. A crucial component of such cloud migrations is the transition of databases to be hosted online. In this work, we consider the difficulties of this migration for SQL databases. While SQL is one of the prominent methods for storing database procedures, there are a plethora of different SQL dialects (e.g., MySQL, Postgres, etc.) which can complicate migrations when the on-premise SQL dialect differs to the dialect hosted on the cloud. Tools exist by common cloud provides such as AWS and Azure to aid in translating between dialects in order to mitigate the majority of the difficulties. However, these tools do not successfully translate $100\%$ of the code. Consequently, software engineers must manually convert the remainder of the untranslated database. For large organizations, this task quickly becomes intractable and so more innovative solutions are required. We consider this challenge a novel yet vital industrial research problem for any large corporation that is considering cloud migrations. Furthermore, we introduce potential avenues of research to tackle this challenge that have yielded promising preliminary results.

We study the problem of translating an image-based, step-by-step assembly manual created by human designers into machine-interpretable instructions. We formulate this problem as a sequential prediction task: at each step, our model reads the manual, locates the components to be added to the current shape, and infers their 3D poses. This task poses the challenge of establishing a 2D-3D correspondence between the manual image and the real 3D object, and 3D pose estimation for unseen 3D objects, since a new component to be added in a step can be an object built from previous steps. To address these two challenges, we present a novel learning-based framework, the Manual-to-Executable-Plan Network (MEPNet), which reconstructs the assembly steps from a sequence of manual images. The key idea is to integrate neural 2D keypoint detection modules and 2D-3D projection algorithms for high-precision prediction and strong generalization to unseen components. The MEPNet outperforms existing methods on three newly collected LEGO manual datasets and a Minecraft house dataset.

Google Translate is an amazing feat of engineering, which uses artificial intelligence to translate speech and text from a chosen language into another. In most cases, Google Translate's own interface embedded in Google Search or on translate.google.com Again, as in other Case Studies presented here, Google Sheets comes to the rescue! Other than formatting the file to your liking, you can create some drop-down lists for the Source and Target languages. This will help you being more productive as you do not need to search for the language codes every time you want to change them. In my case, I used the Data Validation feature using as a criterion a List from a Range.

Recent research suggests that most languages that have ever existed are no longer spoken. Dozens of these dead languages are also considered to be lost, or "undeciphered" -- that is, we don't know enough about their grammar, vocabulary, or syntax to be able to actually understand their texts. Lost languages are more than a mere academic curiosity; without them, we miss an entire body of knowledge about the people who spoke them. Unfortunately, most of them have such minimal records that scientists can't decipher them by using machine-translation algorithms like Google Translate. Some don't have a well-researched "relative" language to be compared to, and often lack traditional dividers like white space and punctuation.

Recent research suggests that most languages that have ever existed are no longer spoken. Dozens of these dead languages are also considered to be lost, or "undeciphered" -- that is, we don't know enough about their grammar, vocabulary, or syntax to be able to actually understand their texts. Lost languages are more than a mere academic curiosity; without them, we miss an entire body of knowledge about the people who spoke them. Unfortunately, most of them have such minimal records that scientists can't decipher them by using machine-translation algorithms like Google Translate. Some don't have a well-researched "relative" language to be compared to, and often lack traditional dividers like white space and punctuation.

Recent research suggests that most languages that have ever existed are no longer spoken. Dozens of these dead languages are also considered to be lost, or "undeciphered" -- that is, we don't know enough about their grammar, vocabulary, or syntax to be able to actually understand their texts. Lost languages are more than a mere academic curiosity; without them, we miss an entire body of knowledge about the people who spoke them. Unfortunately, most of them have such minimal records that scientists can't decipher them by using machine-translation algorithms like Google Translate. Some don't have a well-researched "relative" language to be compared to, and often lack traditional dividers like white space and punctuation.

Humans and computers don't understand data in the same way, and an active area of research in AI is determining how AI "thinks" about data. For example, the recent Quanta article Where We See Shapes, AI Sees Textures discusses an inherent disconnect between how humans and computer vision AI interpret images. The article addresses the implicit assumption many people have that when AI works with an image, it interprets the contents of the image the same way people do- by identifying the shapes of the objects. However, because most AI interprets images at a pixel level, it is more intuitive for the AI to label images by texture (i.e., more pixels in an image represent an object's texture than an object's outline or border) than by shape. Another useful example of this is in language.

Three years ago when someone mentioned the idea of artificial intelligence most people would assume they were going to start referencing Star Wars, Robocop, Terminator, Space Odyssey and the Jetsons. In 2017 A.I. has taken on an entire new trend as more businesses start to invest in the technology and more references are being made to A.I. with tools and technology we use everyday.

I recently confronted this when I began reading about maximum causal entropy as part of a project on inverse reinforcement learning. Many of the terms were unfamiliar to me, but as I read closer, I realized that the concepts had close relationships with statistics concepts. This blog post presents a table of connections between terms that are standard in statistics and their related counterparts in machine learning. Understanding a result in machine learning can help to avoid reinventing the wheel in statistics and vice versa. My ability to understand inverse reinforcement learning benefited from my training in statistics because I was able to translate machine learning terminology into statistical terminology.

Three years ago when someone mentioned the idea of artificial intelligence most people would assume they were going to start referencing Star Wars, Robocop, Terminator, Space Odyssey and the Jetsons. In 2017 A.I. has taken on an entire new trend as more businesses start to invest in the technology and more references are being made to A.I. with tools and technology we use everyday. What tools or technology do I use today that leverages artificial intelligence? What does all this mean for businesses today and how should brands start embracing for a future of digital transformation powered by A.I.? What are some things that will be automated completely by A.I.? (Paperboy?) I answer these questions and more in part 1 of 4 in my "Translate The Geek-Speak" series on the FOMOFanz Podcast.