In this innovative practice work-in-progress paper, we compare two different methods to teach machine learning concepts to undergraduate students in Electrical Engineering. While machine learning is now being offered as a senior-level elective in several curricula, this does not mean all students are exposed to it. Exposure to the concepts and practical applications of machine learning will assist in the creation of a workforce ready to tackle problems related to machine learning, currently a hot topic in industry. Preliminary assessments indicate that this approach promotes student learning. While students prefer the proposed side-by-side teaching approach, numerical comparisons show that the workshop approach may be more effective for student learning, indicating that further work in this area is required.

Google has started making good on its promise to update and optimize 20 of its apps for tablets. The tech giant has rolled out a number of new features for Google Drive, Docs, Sheets, Slides and Keep, which all take advantage of tablets' larger screens. They're tools you can use to make it easier to juggle multiple tasks and to transfer content from one app to another when you have two windows open side-by-side. That could make writing up notes or reports go much quicker than before. If you need to upload anything to Google Drive, you can simply open the app in a split window and then drag-and-drop the files in.

This ongoing debate of marketer vs machines seems to be a little skewed to me. The idea of machines, as we know it comes from our knowledge based on popular culture. Movies like 2001: A Space Odyssey, Westworld, Alien, etc. have shown us what machines and AI can do. Needless to say, the portrayal of machines in popular culture show what people think of them in reality. These serve as cautionary tales in case we actually start putting our faith in machines.

Flask used to come to mind when data scientists want to spin up a python-based data science app, but there is a better option now. To create an interactive facade for a machine learning or visualization script, Streamlit is way faster, since it removed the need to write any front-end code. Now we'll go through step-by-step how to build a Streamlit app. I will also review some pros and cons of Streamlit. Anyone who wants to put an interactive user interface or visible facade to the python scripts. Streamlit can be used to built machine learning/AI apps or display exploratory/analytical data visualizations or both at the same time.

As home to three top-ranked supercomputers of the last decade, the US Department of Energy's (DOE's) Oak Ridge National Laboratory (ORNL) has become synonymous with scientific computing at the largest scales. Getting the most out of these science machines, however, requires a willingness to experiment with problems and systems of every size and scale. This is especially important as technology vendors introduce new system architectures and as scientists' problem-solving toolkit expands to include artificial intelligence (AI) and advanced data analysis. In that spirit, ORNL recently installed two NVIDIA DGX-2 systems, powerful GPU-accelerated appliances that will provide ORNL researchers with enhanced opportunities to conduct science--machine learning and data-intensive workloads in particular. The appliances will also provide an onramp to ORNL's Summit--the world's most powerful supercomputer--by enabling smaller and more experimental projects to be developed and tested before running on the 200-petaflop machine.

After spending more than 100 hours testing LED TVs, including new 2018 models, we think the TCL 6-Series--available in both a 55-inch and 65-inch size--is the best value we have ever seen in a TV series. It produces images with noticeably more detail, brightness, and color than most TVs that cost hundreds more--in fact, even when viewed side-by-side with TVs that cost twice as much, we still prefer the TCL. After the success of the 2017 version, we had high hopes for the 2018 TCL 6-Series TVs, and overall they have delivered. They offer superb performance for their price, including high dynamic range support for both HDR10 and Dolby Vision formats that looks incredible in use. They also include our favorite built-in streaming media interface from Roku, so you don't need a separate device. With excellent performance and no serious flaws, the TCL 6-Series is an easy recommendation. If you want a more accurate image with better motion clarity and you're willing to spend more than twice as much as the TCL for it, you should consider the Sony X900F. The Sony also comes in 49-inch, 55-inch, 65-inch, 75-inch, and 85-inch versions for those looking for a bigger screen than you can get from TCL. With HDR content, its highlights are even brighter and more saturated than TCL's. The price increase is steeper than the image quality increase, though. The TCL is easier to set up, however, and the Sony's Android TV interface, though it offers useful voice search, is harder to use than TCL's Roku interface. I've been reviewing TVs and home theater equipment since 2008. I am an ISF Level II Certified Calibrator, so I am aware of what makes for a good TV image and how to get those things out of a TV. I have all the necessary test equipment and software to provide objective measurements to back up my subjective opinions. Additionally, I enlisted my non-videophile neighbors to take a look at our finalists to make sure our priorities were in line with what normal people look for in a TV.

Fascinating footage has been released of a robot's-eye-view of a driverless vehicle trial at Heathrow Airport, side-by-side with how a human driver would see the routes it took. The clip comes from a'cargopod' vehicle that spent three and a half weeks running autonomously along a cargo route around the airside perimeter. The trial collected over 200km of data for Heathrow, cargo operator IAG Cargo and the software firm providing the self-driving tech, Oxford-based Oxbotica. Fascinating footage has been released of a robot's-eye-view of a driverless vehicle trial at Heathrow Airport, side-by-side with how a human driver would see the routes it took The clip comes from a'cargopod' vehicle, pictured, that spent three and a half weeks running autonomously along a cargo route around the airside perimeter The trial was designed to further understanding about how autonomous vehicles could work in an airside environment so opportunities for their use can be maximised. Lynne Embleton, CEO at IAG Cargo, said: 'Technology is evolving at an incredible pace.

A survey of 500 chief information officers (CIOs) from around the world by ServiceNow has found that machine learning has arrived in the enterprise, and is making material contributions to everyday work. To realise its full value, technology leaders must find skilled talent to work side-by-side with machines, in addition to redesigning their organisations and processes. CIOs were interviewed in 11 countries across 25 industries, including 46 CIOs in the UK, to uncover the competitive benefits of adopting machine learning and hear how those leaders are driving results. See also: Government CIO I.T. budget breakdown: Gartner IDC estimates that investment in machine learning will nearly double by 2020, and recent analysis shows that machine learning specialists are among the fast-growing roles in IT. Humans are working side-by-side with smart machines for better accuracy, speed and growth of business.

The world of AI, robotics and cognitive computing are changing business even faster than we thought. JPMorgan Chase & Co now uses software to perform the mind-numbing job of interpreting commercial loans, reducing 360,000 hours of lawyer time each year. AI software can now identify leukemia in photos and X-rays, learning faster than technicians. And the stories go on and on. Is this real and widespread around the world?

Some enterprises use Clarabridge to mine customer data, manage customer experience, and see sentiment analysis. While Clarabridge provides an intelligence platform, Signals is a more powerful solution platform in unifying customer voice. While sentiment analysis is a key function of Signals, its deep machine learning capability allows you to do something more organic. It enables you to listen to your customer data from the ground up and identify trends and patterns as they emerge. Signals results are displayed directly in front of the user.