What's the first thing that comes to mind when you read the words'artificial intelligence'? Do you think of an algorithm that could solve climate change, or of HAL from 2001: A Space Odyssey? My point is: AI has become a loaded term, as has data. People are weary, even fearful, of new technology – but then, that's nothing new. According to one study, 47% of people believe the rate of technological innovation is happening too fast.

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

Judea Pearl is a professor at UCLA and a winner of the Turing Award, that's generally recognized as the Nobel Prize of computing. He is one of the seminal figures in the field of artificial intelligence, computer science, and statistics. He has developed and championed probabilistic approaches to AI, including Bayesian Networks and profound ideas in causality in general. These ideas are important not just for AI, but to our understanding and practice of science. But in the field of AI, the idea of causality, cause and effect, to many, lies at the core of what is currently missing and what must be developed in order to build truly intelligent systems.

Dr. Iain Brown is the Head of Data Science for SAS UK&I and Adjunct Professor of Marketing Analytics at the University of Southampton. For the last decade he has been working closely with the financial services sector, providing thought leadership on the topics of risk, AI and machine learning. During his time at SAS he has been involved in driving innovation in AI and the corresponding fields of machine learning, computer vision and natural language understanding through the delivery of numerous projects. He is also a contributor to SAS' blog and an active member of the AI community on Twitter. In a wide-ranging conversation about the applications of machine learning in the financial services sector, Iain offered some helpful advice around the integration of AI into business models.

"Q: How worried do you think we humans should be that machines will take our jobs? A: It depends what role machine intelligence will play. Machine intelligence in some cases will be useful for solving problems, such as translation. But in other cases, such as in finance or medicine, it will replace people." This Q&A is taken from Tom Standage's description of how he interviewed AI (language model GPT-2) for The Economist The World in 2020. As readers of this column's annual roundup of AI predictions know, this year's first installment of 120 AI predictions for 2020 featured my interview of Amazon AI in which Alexa performed slightly better than the previous year. For the new list of 99 additional predictions, I repeated Standage's question to Alexa, and got the response "Hmm, I'm not sure." The following AI movers and shakers are a lot more confident in what the near future of machine intelligence will look like, from robotic process automation (RPA) to human intelligence augmentation (HIA) to natural language processing (NLP).

What do you think when someone asks you about empathy? Do you struggle to find its meaning or does it come to you naturally? In the age of artificial intelligence, do our AI systems need empathy? If so, what are some use cases where empathy can be most helpful in AI Systems? When we read a book out loud to our children, they can hear the emotions we imbue into the passages.

Climate change activist Greta Thunberg has been named one of the ten most influential people in science in 2019 by the journal Nature. The 16 year old has been named alongside a neurologist who brought pig brains back to life and a palaeontologist who shook up humanity's family tree. The prestigious British science journal, which celebrated its 150th anniversary this year, says the Swedish campaigner'channelled the rage of a generation'. She had outshone scientists who couldn't'galvanise global attention' the way she did and many are cheering her along, according to Nature. The ten most influential list also includes a physicist building quantum computers, a biologist editing genes in adult humans and a microbiologist fighting Ebola.

At the Tackling Climate Change workshop at this year's NeurIPS conference, some of the top minds in machine learning came together to discuss the effects of climate change on life on Earth, how AI can tackle the urgent problem, and why and how the machine learning community should join the fight. The panel included Yoshua Bengio, MILA director and University of Montreal professor; Jeff Dean, Google's AI chief; Andrew Ng, cofounder of Google Brain and founder of Landing.ai; and Cornell University professor and Institute for Computational Sustainability director Carla Gomes. The Tackling Climate Change workshop explored a wide range of topics, from the use of deep reinforcement learning to improve performance for ride-hailing services like Uber and Lyft to the application of deep learning to predict wildfire risk, detect avalanche deposits, improve plane efficiency with better wind forecasts, and conduct a global census of solar farms. The workshop is put together by Climate Change AI, a group that hosts workshops at AI research conferences and a forum for collaboration between machine learning practitioners and people from other fields. One essential step in better addressing the world's pressing challenges, says Bengio, is changing the way AI research is valued.

I recently got interviewed over email by one of my mentees. I thought their questions were pretty interesting and worth sharing with the community. This session focused on ClawBack and was done for a college media class assignment. I hope you enjoy the interview as much as I did giving it. ClawBack is a platform for helping organizations detect data leaks.

Editor's Note: All opinion section content reflects the views of the individual author only and does not represent a stance taken by The Collegian or its editorial board. Letters to the Editor reflect the view of a member of the campus community and are submitted to the publication for approval. I am writing this essay to bring awareness and recognition to a fast-approaching topic in the field of military technology -- weaponized artificial intelligence. Weaponized AI is any military technology that operates off a computer system that makes its own decisions. Simply put, anything that automatically decides a course of action against an enemy without human control would fall under this definition. Weaponized AI is a perfect example of a sci-fi idea that has found its way into the real world and is not yet completely understood.