Note: This interview was edited and condensed for clarity. As part of our media partnership with Tecton's apply(conf), RTInsights recently had the opportunity to speak with Sarah Catanzaro, General Partner at the venture firm Amplify Partners. The firm has invested in data startups OctoML, Einblick, Hex, among others. Prior to venture capital, she was the Head of Data at Mattermark. She started her career in counterterrorism.

Imran has been a part of cutting-edge research about Algorithms and Machine Learning for the last many years. He completed his PhD in 2010 in which he proposed a new Linear Programming based algorithm which can be used to optimally assign resources in a large scale cloud computing environment. In 2017, Imran developed a realtime analytics framework named StreamSensing. He has since authored multiple research papers that use StreamSensing to process multimedia data for various Machine Learning Algorithms. Imran is currently working at Advanced Analytics Solution Center (A2SC) at Canadian Federal Government as a Data Scientist where he is using Machine Learning Algorithms for critical use-cases. Imran is a visiting professor at Carleton University, Ottawa. Imran has also been teaching for Google and Learning Tree for the last many years. The topics Imran teaches include Algorithms, Cloud Computing and Deep Learning. Over his career, Imran has written many research papers and a couple of his recent papers have won the best paper award. Imran also regularly writes blogs on selected IT topics. In addition to his professional work, Imran is into Nature Photography. Over the years he has taken thousands of photos about nature. Imran's passion is to find a way to make technology work for the betterment of humanity. This passion is the main motivation behind his research.

Security experts are on the alert for the next evolution of social engineering in business settings: deepfake employment interviews. The latest trend offers a glimpse into the future arsenal of criminals who use convincing, faked personae against business users to steal data and commit fraud. The concern comes following a new advisory this week from the FBI Internet Crime Complaint Center (IC3), which warned of increased activity from fraudsters trying to game the online interview process for remote-work positions. The advisory said that criminals are using a combination of deepfake videos and stolen personal data to misrepresent themselves and gain employment in a range of work-from-home positions that include information technology, computer programming, database maintenance, and software-related job functions. Federal law-enforcement officials said in the advisory that they've received a rash of complaints from businesses.

Joseph Weizenbaum (8 January 1923 – 5 March 2008) was a German American computer scientist and a professor at MIT. The Weizenbaum Award is named after him. He is considered one of the fathers of modern artificial intelligence. Born in Berlin, Germany to Jewish parents, he escaped Nazi Germany in January 1936, emigrating with his family to the United States. He started studying mathematics in 1941 at Wayne State University, in Detroit, Michigan.

For PC Magazine's charter issue(Opens in a new window) in early 1982, the newly minted editor-in-chief and publisher David Bunnell flew to Seattle to interview a fresh-faced, 26-year-old Bill Gates, the president and co-founder of a little software company called Microsoft. Bunnell's goal with this exclusive interview was to understand the part Microsoft and its software played in the development of the groundbreaking IBM PC that was born less than a year earlier. After all, that IBM PC was the namesake of Bunnell's new publication. In the interview, the two discuss how much fun it was for Bill and his team to contribute to the IBM project, how gratifying it was to have been part of it, and how the IBM and Microsoft teams worked together to actually get it done. They even speak of shooting jokes back and forth via an early form of email used for communication between the two teams. Besides recalling many of the gritty details of how the software and hardware were developed together (it was a two-hour interview!),

GT caught up with state technology leaders at the recent National Association of State Chief Information Officers Midyear conference. Here's what they had to say about artificial intelligence, chatbots, blockchain and other headline-grabbing technologies. ARTIFICIAL INTELLIGENCE Amanda Crawford, Texas: We use AI today. We certainly use robotic process automation in a variety of applications and projects across the state. One of the exciting areas for us is using AI for security.

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As computers get more powerful, we are increasingly using them to make predictions. The software that makes these predictions is often called artificial intelligence. It's interesting that we call it "intelligence," because other tasks we assign to computers--computing huge numbers, running complex simulations--are also things that we label as "intelligence" when humans do them. For instance, my kids are graded on their intelligence at school based on their ability to do complex mathematical calculations. When we let computers project into the future and make their own decisions about what step to take next--what chess move to make, what driving route to suggest--we seem to want to call it artificial intelligence.

The encounter with the Specifying Integrating Generalizing Mass Array took place not in Sigma's supercooled chamber, but through the laptop. My interview with Sigma was full of irony. I hoped I was not being rude. Anything else I can help you with?" The programming of AI's may have made them smart, I thought, but not polite. "It is a word used specifically to distinguish living beings from inanimate things.

It is therefore critical that we question vague conceptions of the field as value-neutral or universally beneficial, and investigate what specific values the field is advancing. In this paper, we present a rigorous examination of the values of the field by quantitatively and qualitatively analyzing 100 highly cited ML papers published at premier ML conferences, ICML and NeurIPS. We annotate key features of papers which reveal their values: how they justify their choice of project, which aspects they uplift, their consideration of potential negative consequences, and their institutional affiliations and funding sources. We find that societal needs are typically very loosely connected to the choice of project, if mentioned at all, and that consideration of negative consequences is extremely rare. We identify 67 values that are uplifted in machine learning research, and, of these, we find that papers most frequently justify and assess themselves based on performance, generalization, efficiency, researcher understanding, novelty, and building on previous work. We present extensive textual evidence and analysis of how these values are operationalized. Notably, we find that each of these top values is currently being defined and applied with assumptions and implications generally supporting the centralization of power. Finally, we find increasingly close ties between these highly cited papers and tech companies and elite universities.