Ahead of this year's AI Summit London, part of London Tech Week, we invited three Enterprise AI experts to join us for the London Tech Week Digital Series, to discuss responsible AI for business. This webinar is moderated by Aditya Kaul, Research Director at Tractica, who has 12 years' experience in technology market research with a primary focus on artificial intelligence & robotics. Joining Aditya is Ivana Bartoletti, Founder of the Women Leading in AI Network, and Udai Chilamkurthi, Lead Architect for Retail & Logistics at one of the UK's largest supermarket chains, Sainsbury's. In this on-demand webinar, you'll learn how your business can build an ethical framework for responsible AI & unlock the full potential of AI & Machine Learning to transform your enterprise. By accessing this free on-demand webinar by the AI Summit, you'll automatically receive a 20% discount to the upcoming AI Summit San Francisco (Palace of Fine Arts, 25 - 26 September 2019).

Education has not had a make-over in over a century. Some schools still advocate for factory-style instruction. Bureaucratic red tape and top-down initiatives consume teachers' time, leaving little left for instruction. No industry is more ready for a revolution than education. The fourth revolution in education is here, and it's called artificial intelligence.

BENGALURU: A thermal physics class is in progress at Grade 8B of Indus International School, Bengaluru. The physics teacher, Murali Subramanian, is hovering over the children but conducting lessons at the centre of the classroom is Eagle 2.0, a humanoid robot, which could perhaps be the first in the country to be a teacher assistant. We will focus on thermal physics today!" says Eagle 2.0, moving its head and body in robotic movements. Clad in a white top, black skirt and scarf around her neck, she is capable of two-way interaction: She takes queries from students and asks the class questions, and reacts to the answers she receives. On a screen, a PowerPoint presentation is in sync with her class. But, a better answer can be...," she tells a student who answers her question.

Artificial intelligence (AI) and machine learning are amazing technologies that are revolutionizing practically every field of human activity. Intelligent machines can assist or downright substitute humans in literally all tasks, from business and commerce to health care, environment, communications, and any endeavors we can imagine. Understanding AI, while this tech is still in its prime days, is a great way to boost a career in technology. Professionals who can build thinking machines able to get the most value from the immense vaults of unstructured data currently floating around are highly sought after by employers across the globe. Whether you already have experience in the technology field or you are a student with little or no background in AI and programming, there are many online courses available to outpace your competition and find the job of your life.

We first created our chatbots for learning more than 2 years ago, to experiment with the pedagogical impact on learning. What we found was astonishing. These ratings would have been extremely commendable for classroom training and with online learning, typical ratings are usually much lower. With our newly created chatbots for learning and Artificial Intelligence (AI), we also won 2 national tech-enabled learning innovation competitions in Singapore at the national level (in 2017 and 2018). The message that we received was the need for users to see eLearning as interaction with human experts rather than with books and chatbots plugged that gap with persona-based chatbots. This conversational approach made learning fun, less formal, more timely and customized.

Kathleen Siminyu is a data scientist & machine learning engineer who is Regional Coordinator for the Artificial Intelligence for Development – Africa Network. She is Co-Founder of the Nairobi Women in Machine Learning & Data Science community, and part of the Deep Learning Indaba Steering Committee. Her other interests include natural language processing for African languages and low-cost hardware robotics. We share this story as a demonstration of how AI can indirectly bring people together and empower communities instead of downgrade, divide, or discriminate against them. We believe that community leaders have an important role to play in defining humanity's place in a world of algorithms.

Various strategies for active learning have been proposed in the machine learning literature. In uncertainty sampling, which is among the most popular approaches, the active learner sequentially queries the label of those instances for which its current prediction is maximally uncertain. The predictions as well as the measures used to quantify the degree of uncertainty, such as entropy, are almost exclusively of a probabilistic nature. In this paper, we advocate a distinction between two different types of uncertainty, referred to as epistemic and aleatoric, in the context of active learning. Roughly speaking, these notions capture the reducible and the irreducible part of the total uncertainty in a prediction, respectively. We conjecture that, in uncertainty sampling, the usefulness of an instance is better reflected by its epistemic than by its aleatoric uncertainty. This leads us to suggest the principle of "epistemic uncertainty sampling", which we instantiate by means of a concrete approach for measuring epistemic and aleatoric uncertainty. In experimental studies, epistemic uncertainty sampling does indeed show promising performance.

State-of-the-art methods for Convolutional Sparse Coding usually employ Fourier-domain solvers in order to speed up the convolution operators. However, this approach is not without shortcomings. For example, Fourier-domain representations implicitly assume circular boundary conditions and make it hard to fully exploit the sparsity of the problem as well as the small spatial support of the filters. In this work, we propose a novel stochastic spatial-domain solver, in which a randomized subsampling strategy is introduced during the learning sparse codes. Afterwards, we extend the proposed strategy in conjunction with online learning, scaling the CSC model up to very large sample sizes. In both cases, we show experimentally that the proposed subsampling strategy, with a reasonable selection of the subsampling rate, outperforms the state-of-the-art frequency-domain solvers in terms of execution time without losing the learning quality. Finally, we evaluate the effectiveness of the over-complete dictionary learned from large-scale datasets, which demonstrates an improved sparse representation of the natural images on account of more abundant learned image features.

The rapid growth of artificial intelligence and automation presents threats -- and opportunities -- for workers and businesses in the Miami Valley. More than 31,600 people in the Dayton metro area work in the five largest occupations at high risk of automation, according to data the Brookings Institution prepared exclusively for the Dayton Daily News. Those jobs include food preparation, waiters, stock clerks, tractor-trailer truck drivers and accounting clerks. But about 34,600 people in the region that includes Montgomery, Greene and Miami counties work in the largest low-risk occupations. Those include registered nurses, freight and stock movers, janitors, customer service representatives and general managers, according to the Brookings data.

A woman's job application is rejected because of a recruiting algorithm that favors men's résumés. A girl dies by suicide after graphic images of self-harm are pushed up on her feed by social media algorithms. A black teen steals something and gets rated high-risk for committing future crime by an algorithm used in courtroom sentencing, while a white man steals something of similar value and gets rated low-risk. In recent years, advances in computer science have yielded algorithms so powerful that their creators have presented them as tools that can help us make decisions more efficiently and impartially. But the idea that algorithms are unbiased is a fantasy; in fact, they still end up reflecting human biases.