A leading expert in artificial intelligence has issued a stark warning against the use of race- and gender-biased algorithms for making critical decisions. Across the globe, algorithms are beginning to oversee various processes from job applications and immigration requests to bail terms and welfare applications. Military researchers are even exploring whether facial recognition technology could enable autonomous drones to identify their own targets. However, University of Sheffield computer expert Noel Sharkey told the Guardian that such algorithms are'infected with biases' and cannot be trusted. Calling for a halt on all AI with the potential to change people's lives, Professor Sharkey instead advocates for vigorous testing before they are used in public.

How different is KaziQuest system to other Job Search apps? Job searching in Kenya and in the world, in general, has taken a more technological angle. The better a system is able to use machine learning and artificial intelligent the better it is for its users to narrow down into the specific needs for their search. We have partnered with Google, using Google's expertise in machine learning to provide faster, more relevant results for workers looking for jobs on App.KaziQuest.com It is a two way, a win-win situation for both the job seeker and the employer. At KaziQuest we have a solution for the needs of these two entities.

According to media reports more than 3,200 students have applied for the school in the first week admissions were open. Many of the applicants came from the UAE, Saudi Arabia, Algeria, Egypt, India, and China. In October Abu Dhabi announced the Mohamed bin Zayed University of Artificial Intelligence, which will enable graduate students, businesses, and governments to advance AI. The university is named after the Crown Prince of Abu Dhabi Mohamed bin Zayed Al Nahyan, who is an advocate for developing human capital through science. The school aims to create a new model of academia and research for AI and to "unleash AI's full potential."

The world's most prestigious machine learning conference wraps up in Vancouver this weekend. Synced takes a look at the numbers associated with NeurIPS 2019. This year marked the 33rd annual NeurIPS conference. Communication Co-chair Michael Littman told attendees: "This year is only the third time NeurIPS has had a formal relationship with the press. Also, there were 3 awards -- Outstanding Paper, Outstanding New Directions Paper, and Test of Time.

Various neural network based methods are capable of anticipating human body motions from data for a short period of time. What these methods lack are the interpretability and explainability of the network and its results. We propose to use Dynamic Mode Decomposition with delays to represent and anticipate human body motions. Exploring the influence of the number of delays on the reconstruction and prediction of various motion classes, we show that the anticipation errors in our results are comparable or even better for very short anticipation times ($<0.4$ sec) to a recurrent neural network based method. We perceive our method as a first step towards the interpretability of the results by representing human body motions as linear combinations of ``factors''. In addition, compared to the neural network based methods large training times are not needed. Actually, our methods do not even regress to any other motions than the one to be anticipated and hence is of a generic nature.

A discrete system's heterogeneity is measured by the R\'enyi heterogeneity family of indices (also known as Hill numbers or Hannah-Kay indices), whose units are known as the numbers equivalent, and whose scaling properties are consistent and intuitive. Unfortunately, numbers equivalent heterogeneity measures for non-categorical data require a priori (A) categorical partitioning and (B) pairwise distance measurement on the space of observable data. This precludes their application to problems in disciplines where categories are ill-defined or where semantically relevant features must be learned as abstractions from some data. We thus introduce representational R\'enyi heterogeneity (RRH), which transforms an observable domain onto a latent space upon which the R\'enyi heterogeneity is both tractable and semantically relevant. This method does not require a priori binning nor definition of a distance function on the observable space. Compared with existing state-of-the-art indices on a beta-mixture distribution, we show that RRH more accurately detects the number of distinct mixture components. We also show that RRH can measure heterogeneity in natural images whose semantically relevant features must be abstracted using deep generative models. We further show that RRH can uniquely capture heterogeneity caused by distinct components in mixture distributions. Our novel approach will enable measurement of heterogeneity in disciplines where a priori categorical partitions of observable data are not possible, or where semantically relevant features must be inferred using latent variable models.

This paper proposes a tentative and original survey of meeting points between Knowledge Representation and Reasoning (KRR) and Machine Learning (ML), two areas which have been developing quite separately in the last three decades. Some common concerns are identified and discussed such as the types of used representation, the roles of knowledge and data, the lack or the excess of information, or the need for explanations and causal understanding. Then some methodologies combining reasoning and learning are reviewed (such as inductive logic programming, neuro-symbolic reasoning, formal concept analysis, rule-based representations and ML, uncertainty in ML, or case-based reasoning and analogical reasoning), before discussing examples of synergies between KRR and ML (including topics such as belief functions on regression, EM algorithm versus revision, the semantic description of vector representations, the combination of deep learning with high level inference, knowledge graph completion, declarative frameworks for data mining, or preferences and recommendation). This paper is the first step of a work in progress aiming at a better mutual understanding of research in KRR and ML, and how they could cooperate.

Artificial intelligence can perform feats that seem like sorcery. AI can drive cars and fly drones. It can compose original music, write poetry that isn't too awful, and design recipes that do sound awful (blueberry and spinach pizza, anyone?). AI can do some things better than humans: lip reading, diagnosing diseases such as pneumonia and some cancers, transcribing speech, and playing Jeopardy!, Go, Texas Hold'em, and a variety of video games. AI software can even learn to make its own AI software.

Technology develops differently in Japan. While US tech giants have been grabbing artificial intelligence headlines, a business AI sector has been quietly maturing in Japan, and it is now making inroads into America. Today we sit down again with Miku Hirano, CEO of Cinnamon, and we talk about how exactly this happened. Interestingly, Cinnamon did not start out as an AI company. In fact, when Miku first came on the show, the company had just launched an innovative video-sharing service. Today, we talk about what lead to the pivot to AI and why even a great idea and a great team is no guarantee of success. We also talk about some of the changing attitudes towards startups and women in Japan, the kinds of business practices AI will never change, and Miku give some practical advice for startups going into foreign markets. It's a great discussion, and I think you will really enjoy it. Welcome to Disrupting Japan, straight talk from Japan's most successful entrepreneurs. Today, we're going to sit down and talk about artificial intelligence with Miku Hirano of Cinnamon. Now, Cinnamon is actually a great example of a successful Japanese startup pivot. When we first sat down with Miku four years ago, she had an innovative micro-video sharing company called Tuya and really, you should go back and listen to that episode. I've put a link on the show notes and it was really a good one.

Nearly 600 years after Chinese monks advanced the spread of knowledge with block printing, there was the Gutenberg Press, changing the dissemination of knowledge forever. And now, nearly 600 years later, Artificial Intelligence is poised to do the same. For many, Artificial Intelligence is clouded in mystery, and bound to big screen killer bots that ultimately decide that humans must be terminated. From HAL 9000, to Skynet, to I, Robot, and back to Skynet again, AI has been popularly framed in how it can hurt humanity, rather than how it can help. Artificial Intelligence aims to train machines to perform tasks with the hallmarks of human intelligence: inference, speech recognition, visual perception, planning, learning, and language comprehension.