Think about it – what's the first thought that comes to your mind when you think about Data Science? Here's what I'm proposing – how about we explore the road less traveled? In this weird and wacky world, our data science work reflects surprising connections, such as – if you are buying diapers then you are most likely to buy beer. Or, people who go to bars are a higher credit risk! Oh, I cannot miss out on this one – Smart people prefer curly fries.

These are troubled times for artificial intelligence developers: never has there been such potential in the field of machine learning, which relies on users' personal information for training--however, data regulation and public perception of digital privacy have never been sterner, either. The 2018 Cambridge Analytica scandal was a watershed moment: personal data from 87 million Facebook users were covertly used for political campaigning. This event, and the frequent news of security breaches in social networks, in operating systems and in cloud servers have eroded public trust. Earlier this year, Google admitted that its employees listen to recordings of conversations held between clients and the company's smart speaker. Technologists are on a quest for privacy-protecting artificial intelligence, which has led to the proposal of new techniques like federated learning.

Amid rage brewing on both sides of the political spectrum over testimony by Christine Blasey Ford and Judge Brett Kavanaugh, it would be understandable if you missed some significant artificial intelligence news in Washington D.C. in recent weeks. This week, a group of four senators -- two Democrats, two Republicans -- put forward the AI in Government Act to do things like carry out unique research on federal AI policy, work across agencies, and form an AI advisory board similar to the one created by the European Union earlier this year. The bill has the support of Microsoft, Intel, and the Internet Association, an organization whose members represent some of the biggest tech companies in AI, including Amazon, Facebook, and Google. Last week, Senator Kamala Harris (D-CA) and seven of her colleagues in the House and Senate signed and sent letters to the Federal Bureau of Investigation (FBI), Federal Trade Commission (FTC), and Equal Employment and Opportunity Commission (EEOC) asking questions about their use of facial recognition software. The FTC and EEOC were asked things like how they address claims of discrimination that may be the result of algorithmic bias and if it has received complaints about facial detection in the workplace or as part of hiring practices.

A YouTube video was released this week that explained Bitcoin in a rather creative way using the predictive text from an artificial intelligence model. Botnik Studios released the video as part of a creative series that explores the use of artificial intelligence for creating artistic representations of language. The video was not an attempt to create a machine learning definition of Bitcoin but rather a creative portrayal of the Bitcoin movement in a semi-humorous manner. The predictive text was generated from a corpus of Bitcoin definitions that had been given in the past. These were used as a training dataset and then Botnik Studios creatively generated text from a predictive keyboard.

It's hard to imagine one Death Eater kissing another one on the cheek while more of Voldemort's supporters gather around them and applaud -- unless you're a computer. Botnik Studios, a company that uses algorithms to train computers to behave in certain ways, has produced a brand new chapter from a new Harry Potter book. While a computer can certainly mimic elements of the famous series, though, it can hardly capture J.K. Rowling's magic -- which is why the result, called Harry Potter and the Portrait of What Looked Like a Large Pile of Ash, is so strange and funny that fans are begging for more. Too funny: Botnik Studios used predictive keyboards to write a new chapter in a new Harry Potter book. It's called Harry Potter and the Portrait of What Looked Like a Large Pile of Ash Has that computer been Confunded?

During his 20 years as the New Yorker's cartoon editor, Bob Mankoff developed an interest in the creative potential of artificial intelligence. In 2005, he helped found the magazine's cartoon caption contest and his desk began receiving between 5,000 and 10,000 entries a week. Mankoff – who studied experimental psychology at university – worked with Microsoft, and Google's DeepMind, on projects that attempted to develop algorithms to distinguish between funny and unfunny submissions. For tech firms, developing machines with a sense of humour makes commercial sense. As electronic assistants and robots play an ever greater role in our lives, we'll want them to be good company.

If the game Cards Against Humanity and those refrigerator poetry magnets had a digital baby bestowed with machine learning, it would look something like Botnik. This Seattle-based startup is actually the comedic offspring of Jamie Brew, previously a head writer for ClickHole, a satirical website connected to The Onion, and Bob Mankoff, cartoon and humor editor of Esquire and former cartoon editor of The New Yorker. "Bob and I started Botnik after a series of long phone calls converging on the idea that comedy writing isn't a problem that an algorithm can solve," Brew said. "We didn't really care for fully automatic creativity (such as Google DeepMind's attempt to win The New Yorker Caption Contest) and were far more interested in human-machine collaboration." Botnik builds a "predictive keyboard" of words taken from various sources -- beauty ads, nature shows, famous poets, dialogue from "Seinfeld" episodes and even combinations of sources, including the unlikely triumvirate that is Beowulf/Maya Angelou/forklift manual.

Welcome to another part of the series. This time we will build a model that predicts the next word (a character actually) based on a few of the previous. We will extend it a bit by asking it for 5 suggestions instead of only 1. Similar models are widely used today. You might be using one without even knowing! Our weapon of choice for this task will be Recurrent Neural Networks (RNNs).

Artificial intelligence (AI) is becoming increasingly ubiquitous within the technology industry, with capabilities that are much more practical than most consumers may think. Smarter e-mail spam filters and autonomous vehicles are just two examples of how deep-learning systems and AI technologies enable machines to better interact with their surrounding environment and provide vast benefits to users. By using a series of layers within a neural network to analyze data, deep-learning systems continue to change the way computers see, hear, identify, and even respond to objects in the real world. While the combination of such skills has made it possible for machines to perform increasingly complicated, human-like functions, the future of deep learning is now being dictated by user-driven input methods. Neural-network algorithms are among the most interesting machine-learning techniques.