Learn more about the book here and watch Shane's presentation on the book here. Learn more about the book here and watch Russel's presentation on the book here. Moving away from the more conceptual books listed, The Hundred-Page Machine Learning Book provides a concise and practical look at the most fundamental questions in ML. In this book, Interpretable Machine Learning Researcher and Ph.D, Christoph Molnar focuses on ML's biggest issue with adoption: that these systems seldom explain their inner-workings meaning a great deal of a machine's processes are hidden within a black box. A free online version of the book can be found here, and author's presentation of the main idea from this book can be found here.

MLOps follows a set of practices to deploy and maintain machine learning models in production efficiently and reliably. While the data science team has a deep understanding of the data, the operations team holds the business acumen. MLOps combines the expertise of each team, leveraging both data and operations skill sets to enhance ML efficiency. According to the Algorithmia report, nearly 22 percent of companies have had ML models in production for one to two years. With practice, MLOps professionals can enhance their skills, and develop a solid pipeline for developing machine learning models.

Early reviews of the new documentary film ROADRUNNER about the late food mogul Anthony Bourdain were overwhelmingly positive. Upon its official release last week, though, it started to get some backlash particularly after filmmaker Morgan Neville said he used artificial intelligence technology to create some quotes in Anthony's voice. In an interview with The New Yorker, Neville explained how his team "created an A.I. model of his [Bourdain's] voice" because there were three quotes wanted for the film that had no previous recordings before. By sending a software company hours of recordings and footage, they were able to splice together these quotes in Anthony's voice. "If you watch the film, other than that line you mentioned, you probably don't know what the other lines are that were spoken by the A.I., and you're not going to know," Neville told The New Yorker: "We can have a documentary-ethics panel about it later."

Adam Cartwright, head of IoT application delivery at Siemens, said: "Acoustic data is complex and challenging. Only by combining Siemens data science experts from multiple sectors, like oil and gas, with Yorkshire Water's deep knowledge of water networks could we train such a powerful AI."

Sorry to do this, but...close your eyes and think about being stuck on hold: the repetitive music, the periodic sales pitches, the reminders to visit the company's website. But that's not all: VC money is eyeing AI for sales reps, too. "Sales intelligence" tools use AI to listen to salespeoples' conversations with customers, then compile insights that can help drive future revenue. Two of the hottest companies in this space are Chorus.ai Some conversations are just too complex for it to boil down into a set of ones and zeros.

Internet is everywhere, anyone from anywhere could access it and learn many things indeed. The same applies to Artificial Intelligence (AI) as well. Anyone with access to the internet could learn and explore the realms of AI without depending on an external factor like a course or maybe a degree. Anyone who has a spark to learn AI in and out could do it just with the readily available sources. This is the exact concept of the Democratization of Artificial Intelligence.

Among various applications of AI technology in the pharmaceutical industry, some are viewed as most important and worth more depth of exploration. The first step in drug development is to understand the biological origin and mechanism of the disease, and then to determine suitable targets through high-throughput technologies such as shRNA screening and deep sequencing, and finally to find relevant patterns through a large number of diverse data sources. This is huge work and often presents an important challenge for traditional methods. Unlike traditional methods, AI can systematically analyze existing literature and data in just a few seconds. This real-time "omics" database analysis can more accurately understand pathological cells and molecular mechanisms, and it can be used for complex diseases such as neurodegenerative diseases.

Since at least 2017, there have been discussions about esports becoming part of the Olympics, even as sports aimed at younger audiences joined the Games, including skateboarding and surfing in Tokyo. This year, those conversations led to esports gaining a more significant presence in professional competition. In April, the IOC announced it would hold virtual auto racing, baseball, cycling, rowing and sailing competitions, ahead of the Summer Games. However, those competitions omitted the most popular game titles in esports, such as "League of Legends" and "Dota 2," and focused instead on games that replicated traditional sports with limited player bases.

Say what you will about Space Jam: A New Legacy, but Don Cheadle really goes for it. He threatens, he cajoles, he chews the scenery with the enthusiasm of a rabid guinea pig. Just fully cannonballs into the role of a spurned genius exacting revenge. In the context of a movie that is, let's say, not on the Criterion short list, Cheadle imbues his character with the sort of fragile humanity you wouldn't expect in a movie that features Porky rapping. Which would be great, except that he's playing lines of code.

Women in tech can supercharge teams' creativity and help them stay under budget, meet deadlines and improve outcomes, studies show, so it's time for more women to pursue tech careers, according to a lead Department of Labor official speaking at GovernmentCIO Media & Research's Women Tech Leaders event Thursday. Kathy McNeill, who leads emerging technology strategy at the agency, said the federal government needs more women in AI to produce accurate data sets and data analysis. "AI is a reflection of those who develop it and the data sets we use," she said during a fireside chat. McNeill provided an example of how Google Translate took the phrase "she is a doctor and he is a babysitter" and translated it to "he is a doctor and she is a babysitter" in another language, to illustrate biases inherent in artificially intelligent algorithms. "A lot of systems were developed 10 to 20 years ago," she said.