Machine Learning is one of the most exciting fields in the world. Every week we discover something new, something amazing, something revolutionary. It's incredible, but it can also be overwhelming. That's why we created This Week in Machine Learning! Each week we publish a curated list of Machine Learning stories as a resource to help you keep pace with all these exciting developments.

In the past human work was preferred over a machine's work because a human was more accurate than a machine. After all a human could look at all angles and make an informed decision, and a machine could not. But enter machine learning today, and a machine might be more useful than a human in shaping customer experiences. Today machine learning can help brands scale their engagement operations and provide increasingly relevant experiences. And the good news is now you don't have to be a software expert to use machine learning.

At Whetlab, a startup acquired by Twitter in 2015, the technique provided a better way of--wait for it--designing neural networks. Designing a neural network is a task of extreme trial and error. It's a difficult and time-consuming undertaking, but GPs and Bayesian optimization can help automate the task. As WhetLab founder and Harvard computer scientist Ryan Adams has said, the startup used "machine learning to improve machine learning." Neural networks can suffer from the "confident error" problem, and in identifying uncertainty, this kind of optimization can help address that problem.

A steady pencil-pour of steamed milk right in the heart of the espresso a Flat White dot in the making. Among many others, you know. One of our core principles is taking care of one another. That means mutual support, both professionally and personally. This ethos extends to our customers, who we figuratively (and sometimes literally) smother in hugs.

If you have used Facebook at all in the past few years you are probably familiar with the app's remarkable Artificial Intelligence capabilities. This is largely present in the face-recognition and friend tag suggestion abilities. Apparently, though, the company has improved this AI so that it is now even more impressive. Indeed, Facebook's director of applied machine learning, Joaquin Candela, shared, in a blog post, that Facebook can now recognize not only particular objects in your photographs, but also scenes and actions now, too. And this is true even within your photo posts that bear no words at all.

Enterprise apps, from CRM to ERP to marketing automation, have long had a common structure. Whether in the cloud or on-premise they are anchored by a database layer, responsible for storing records concerning customers, products, and other entities. At the top is a presentation layer, responsible for displaying lists or summaries of the underlying records, and facilitating the entry of new records. Between these two layers is business logic that determines how to aggregate records, what restrictions to put on record entry, and the like. As a user of these apps you predominantly use the app itself: You log in and manipulate the presentation layer directly to enter or see the information that's relevant to the task at hand.

Analysis Machines have triumphed again. Libratus, a powerful computer program, has crushed its human opponents at a heads-up no-limit Texas hold'em poker tournament held at Rivers Casino in Pittsburgh, Pennsylvania, winning $1,776,250 over 120,000 hands. It's a landmark achievement in AI game playing, said Tuomas Sandholm, co-creator of Libratus and a machine-learning professor at Carnegie Mellon University (CMU). "Heads up no-limit Texas hold'em is – in a way – the last frontier standing within the foreseeable future. Of course, new things can come later. But of all of the games, where AI research has been significantly conducted – by which I mean multiple decades of research – all the other games like Othello, checkers, chess, Go, limit no Texas hold'em, Jeopardy! "But heads up no-limit Texas hold'em remained elusive in that never before has it been possible to beat the absolute top no limit Texas hold'em professionals.

Some day in the not-too-distance future, people will say words like these. There is a moment in humanity's future that is approaching. And in this moment in time, people will no longer have careers or work full-time jobs. A common thread of argument is that "there will always be work for people to do." To some degree, this may be true.

Drones and airports usually go together like oil and water, but you can't say that about Atlanta's air hub. The city has formed a partnership with 3DR, Autodesk and engineering firm Atkins that has drones mapping Hartsfield-Jackson International Airport as part of a planned expansion. The key to making it work was Site Scan, 3DR's autonomous data capturing tech. The drones could capture 2D mosaics and 3D point scans while staying well away from the airliners -- no mean feat when they're flying between runways at the busiest airport in the world (over 100 million passengers per year). If anything, the biggest challenge was getting the green light from extra-wary FAA regulators.

Machine learning, artificial intelligence, deep learning… Unless you've been living under a rock, chances are you've heard these terms before. Indeed, they seem to have become a must for market researchers. Unfortunately, so many precise terms have never meant so little! For computer scientists these terms entail highly technical algorithms and mathematical frameworks; to the layman they are synonyms; but as far as most of us should be concerned, increasingly, they are meaningless. My engineers would severely chastise me if I used these words incorrectly--an easy mistake to make since there is technically no correct or incorrect way to use these terms, only strict and less strict definitions.