Assign a chief AI ethics officer (CAIO) who along with a cross-functional ethics board (including representatives from data science, regulatory, public relations, communications and HR) should be responsible for the designing and implementing AI education activities. The CAIO should also be the "ombudsman" for staff to reach out to in case of fairness concerns, as well as a spokesperson to non-technical staff. Ideally this role should report directly to the CEO for visibility and implementation.

I started my data science journey back in 2016. At that time, machine learning boot camps were much popular. The only requirement of the course was basic Python knowledge. I felt so happy because I used to play with Python codes at that time, so I opted for the course. The usual practice of human beings -- when we're learning something new -- is to try and collect as much material from different places.

Penn State has launched its first degree program in artificial intelligence, a master's degree that is offered online and designed to provide a professional, technical education in developing and deploying AI and machine learning. Penn State is now accepting applications for the Master of Professional Studies in Artificial Intelligence program, which is being offered by the engineering division of the Penn State Great Valley School of Graduate Professional Studies online through Penn State World Campus. The first courses will start in January 2022. Penn State's first academic offering in AI comes at a time of soaring growth in the job market and of increasing presence of AI in everyday products and services, said Colin Neill, a professor of software and systems engineering who is the director of the new program. The World Economic Forum estimates that AI will lead to the creation of 97 million new jobs by 2025.

Eight years ago, Dr Dean Mohamedally, principal teaching fellow at University College London's computer science department, launched a new initiative aimed at ridding computer science programmes of fiction. "It's called the Industry Exchange Network (or IXN): it's a teaching methodology we developed with UCL and Microsoft, whereby we help students to engage with real-world problem-solving," Mohamedally explains. "Computer-science teaching across the country is basically about teaching maths, with very little implementation and activities in the real world. So we set about removing fiction from syllabuses. For instance, if students are learning about data, we give them some synthetic sample data and get them to clean it, make it ready, and see what happens when they use it in a machine learning environment."

Artificial intelligence is one of the most revolutionary technologies of our time and you can make money out of it. Every industry in today's time is powered by AI as businesses are using it as a competitive tool. From chatbots for efficient customer service to using it for analytics to make predictive recommendations, artificial intelligence is leading the world forward. That makes AI an unmissable technology. If you are in the IT industry, you must be habituated to use programming languages, work with different frameworks, build software for companies, and write various codes to escalate problems.

Become an expert and make a difference to society. The University of South Australia (UniSA) is Australia's University of Enterprise. We are South Australia's largest university and one of the very best young universities in the world. At UniSA, we are authentic, resilient, and influential - and we deliver results. We pride ourselves on our dynamic and agile culture, which embraces challenges and thrives on breaking new ground.

The POWER TRIP J. Cafesin A sharp jolt wakes me. I open my eyes, my heart beating hard and fast, but I stay in bed while the room shakes, the walls creak and groan. Bolting for the bedroom threshold in my boxers to huddle in the doorway seems a bit extreme. The rash of quakes…

South Eastern Regional College (@S_ERC) is hosting an online international conference to highlight the need for businesses to identify employee digital skills gaps and provide relevant digital skills training for employees in preparation for the move to Artificial Intelligence (AI) technology within the work environment. The Reskilling Revolution in the Artificial Intelligence Era: The Case for Digital Upskilling is online 10am – 1.00pm GMT, Thursday 17 June. However, more and more businesses are also reaping the benefits of AI whether that's a small business replacing paper-based processes with digital, using drones, virtual reality or augmented reality or a total transformational change. The impact for employees could be devastating, with concerns over job role and security, but by providing relevant digital upskilling training, employers can retain valuable employees minimising uncertainty and job losses." Speakers will include leading experts from the field of Artificial Intelligence who will share their knowledge and expertise.

Due to the challenge of modeling the structure of realistic data, theoretical studies of generalization often attempt to derive data-agnostic generalization bounds or study the typical performance of the algorithm on simple data distributions. The first set of theories derive bounds based on the complexity or capacity of the function class and often struggle to explain the success of modern learning systems which generalize well on real data but are sufficiently powerful to fit random noise [1, 2]. Rather than exploring data-independent worst-case performance, it is often useful to analyze how algorithms generalize typically or on average over a stipulated data distribution [3]. A typical assumption made in this style of analysis is that the data distribution possesses a high degree of symmetry by assuming the data follows a factorized probability distribution across input variables [4]. For example, spherical cow models treat data vectors as drawn from the isotropic Gaussian distribution or uniformly from the sphere while Boolean hypercube models treat data as random binary vectors. Models which study such simplified data distributions have been employed in several classic and recent studies exploring the capacity of supervised learning algorithms and associative memory [5, 6], overfitting peaks and phase transitions in learning [7, 8, 9, 10, 11, 12], and neural network training dynamics [13]. Rather than being distributed isotropically throughout the entire set of ambient dimensions, realistic datasets often lie on low dimensional structures. For example, MNIST and CIFAR-10 lie on surfaces with intrinsic dimension of 14 and 35 respectively [14].

Multi-layered network exploration (MuLaNE) problem is an important problem abstracted from many applications. In MuLaNE, there are multiple network layers where each node has an importance weight and each layer is explored by a random walk. The MuLaNE task is to allocate total random walk budget $B$ into each network layer so that the total weights of the unique nodes visited by random walks are maximized. We systematically study this problem from offline optimization to online learning. For the offline optimization setting where the network structure and node weights are known, we provide greedy based constant-ratio approximation algorithms for overlapping networks, and greedy or dynamic-programming based optimal solutions for non-overlapping networks. For the online learning setting, neither the network structure nor the node weights are known initially. We adapt the combinatorial multi-armed bandit framework and design algorithms to learn random walk related parameters and node weights while optimizing the budget allocation in multiple rounds, and prove that they achieve logarithmic regret bounds. Finally, we conduct experiments on a real-world social network dataset to validate our theoretical results.