Artificial intelligence is a bit of a buzz term these days – but what do people really mean when they say AI? And why should local governments care? First of all, AI is extremely misunderstood. We aren't talking about HAL from "2001: A Space Odyssey," necessarily; we're talking about what Alan Turing speculated about "thinking machines" back in the 1950s. According to the Brookings Institute, AI is generally thought to refer to "machines that respond to stimulation consistent with traditional responses from humans, given the human capacity for contemplation, judgment and intention."

"Mike Kanaan is an influential new voice in the field of AI, and his thoughts paint an insightful perspective. "Kanaan's book makes us aware of the urgent need for international understanding and a formal agreement on AI. Without binding commitments, the future will pose threats, both military and social, that risk our very survival. AI may be a blessing, but it can also be the ultimate curse. The world must agree to draw a red line between the two, and make sure that no one crosses it." "Never have I read a book that did a better job of putting the challenges and prospects of artificial intelligence into context.

I welcome you all to the Cyber Society of Today--a wondrous place where'what' is a possibility, 'how' is full of options, and'when' is a mystery. Despite what you may think, this is a real place. It is here, it is now, and most certainly you are in it. So, buckle up, be open-minded, and enjoy the ride--the doors are locked, and there is no place to hide. In this podcast, Sean and I are following up on an exciting story that we started during one of the panels we hosted at the RSA Conference in San Francisco a few weeks ago.

Deep Neural Network-based systems are now the state-of-the-art in many robotics tasks, but their application in safety-critical domains remains dangerous without formal guarantees on network robustness. Small perturbations to sensor inputs (from noise or adversarial examples) are often enough to change network-based decisions, which was recently shown to cause an autonomous vehicle to swerve into another lane. In light of these dangers, numerous algorithms have been developed as defensive mechanisms from these adversarial inputs, some of which provide formal robustness guarantees or certificates. This work leverages research on certified adversarial robustness to develop an online certified defense for deep reinforcement learning algorithms. The proposed defense computes guaranteed lower bounds on state-action values during execution to identify and choose a robust action under a worst-case deviation in input space due to possible adversaries or noise. The approach is demonstrated on a Deep Q-Network policy and is shown to increase robustness to noise and adversaries in pedestrian collision avoidance scenarios and a classic control task. This work extends our previous paper with new performance guarantees, expanded results aggregated across more scenarios, an extension into scenarios with adversarial behavior, comparisons with a more computationally expensive method, and visualizations that provide intuition about the robustness algorithm.

My friend Robert John, professor of computer science at the University of Nottingham, who has died of liver cancer aged 64, pioneered the use of "type-2 fuzzy sets" in computational intelligence, to establish ways of reasoning algorithmically about linguistic concepts that involve uncertainty – something humans are good at, but computers are not. In the 1990s, while Rob (as he was known to family, though called Bob by work colleagues) was working at De Montfort University, he became involved in research into solving a community transport scheduling problem using fuzzy logic. Working from the foundations laid by Prof Lotfi Zadeh, Rob, through his PhD in 2000 and subsequent work with Prof Jerry Mendel and others, developed the mathematical techniques to use type-2 fuzzy sets. Two papers on type-2 and interval type-2 that he wrote with Mendel are among the most frequently cited and influential in the world on the topic. Rob was a founder member in 1995 of the Centre for Computational Intelligence at De Montfort and led its growth through the 2000s, established his reputation in the Institute of Electrical and Electronics Engineers' conferences and in journals on fuzzy logic, and was promoted over time to deputy dean.

Following the great minds of machine learning can help you discover new things and deepen your knowledge. It's fascinating to learn from the best scientists. Among them, you will find influencers, teachers, business leaders, and even many more. Undeniably their expertise can help to change the world and make it a better place. On this list, you will find not only influencers but also renowned personalities from the world of Data Science.

Mastering Voice Technologies in Healthcare with Rupal Patel, Founder and CEO at VocaliD was automatically transcribed by Sonix with the latest audio-to-text algorithms. This transcript may contain errors. Sonix is the best audio automated transcription service in 2020. Our automated transcription algorithms works with many of the popular audio file formats. Welcome to the Outcomes Rocket podcast, where we inspire collaborative thinking, improved outcomes and business success with today's most successful and inspiring health care leaders and influencers. Saul Marquez: Welcome back to the podcast, Saul Marquez here, and today I have the privilege of hosting Dr. Rupal Patel. She is the Founder and CEO of VocaliD, a voice tech company that provides AI-generated voices with personality. VocaliD's Award winning technology leverages the latest advances in machine learning, signal processing and voice analysis, along with the company's crowdsourced voice bank to create inclusive, diverse and brand aligned voices, organizations and individuals with special needs. I know I hate that voice, that robotic voice, when you dial a number or you try to connect here your physician's office, or even if you're doing a training video, it's annoying and it feels like it lacks effort. Rupal and her team didn't like it either, and they felt there was an opportunity to get beyond just content and focus on delivery and the technologies here. We just have to create the awareness. She began her career as a speech clinician where she became fascinated with the potential of using speech tech and technologies for assistive communications, which then led to a doctorate in speech science at her Interdisciplinary Research applies empirical evidence about speech motor control to develop novel communication technologies. She's currently on leave from Northeastern University, where she's a Tenured Professor, and also she does computer science in the Department of Communication of Sciences and Disordered. She's been named the Top 11 Visionaries in Voice in 2019 by a Voicebot.ai

Artificial Intelligence (AI) represents a drastic change in the technological evolution. At the moment, this technology is used in every field of science. We have seen the use of AI in various arenas such as autonomous vehicles, face recognition and robotics, among others. Similarly, it is also embedded in the medical field in areas such as disease prediction using representational images, i.e. prediction of breast cancer using CNN (Convolutional Neural Networks) in a deep learning model. But currently, we are moving a step forward into the future in order to find the risk of premature death in individuals with underlying chronic diseases.

Xiao-Li Meng, the Whipple V. N. Jones Professor of Statistics, and the Founding Editor-in-Chief of Harvard Data Science Review, is well known for his depth and breadth in research, his innovation and passion in pedagogy, his vision and effectiveness in administration, as well as for his engaging and entertaining style as a speaker and writer. Meng was named the best statistician under the age of 40 by COPSS (Committee of Presidents of Statistical Societies) in 2001, and he is the recipient of numerous awards and honors for his more than 150 publications in at least a dozen theoretical and methodological areas, as well as in areas of pedagogy and professional development. He has delivered more than 400 research presentations and public speeches on these topics, and he is the author of "The XL-Files," a thought-provoking and entertaining column in the IMS (Institute of Mathematical Statistics) Bulletin. His interests range from the theoretical foundations of statistical inferences (e.g., the interplay among Bayesian, Fiducial, and frequentist perspectives; frameworks for multi-source, multi-phase and multi- resolution inferences) to statistical methods and computation (e.g., posterior predictive p-value; EM algorithm; Markov chain Monte Carlo; bridge and path sampling) to applications in natural, social, and medical sciences and engineering (e.g., complex statistical modeling in astronomy and astrophysics, assessing disparity in mental health services, and quantifying statistical information in genetic studies). Meng received his BS in mathematics from Fudan University in 1982 and his PhD in statistics from Harvard in 1990.

Jack Schofield, the Guardian's former computer editor and author of its technology advice column, Ask Jack, for almost 20 years, has died aged 72. Schofield was taken to hospital following a heart attack on Friday night and died on Tuesday afternoon. The Guardian's editor-in-chief, Katharine Viner, said: "Jack Schofield was one of the first true technology and computing experts in British journalism. In more than 35 years writing for the Guardian, he saw (and foresaw) the rise of personal computers, the advent of the internet, Google, smartphones and much more. His Ask Jack column was an essential and expert guide for generations of Guardian readers. Our thoughts are with Jack's family and friends at this sad time."