The world's largest AI research conference is underway in Vancouver, Canada. Researchers are presenting more than 1,400 papers at the Neural Information Processing Systems (NeurIPS) conference, ranging from work that organizers believe has had the greatest impact over the past decade to Yoshua Bengio's continued march toward consciousness for deep learning. But even as the conference showed theoretical research and neuroscience-related papers on the rise alongside categories like algorithms and deep learning, the mushrooming of the event itself -- and the associated growing pains -- was a constant theme, and it speaks to the growth of the AI field in general. Organizers said that at the start of the conference Sunday, they expected about 400 people to show up for registration. All told, NeurIPS 2019 welcomed 13,000 attendees, up 40% from the prior year.

AI having applications in various sectors including agriculture has completely transformed the approaches of the agriculture market. AI in Agriculture helps the farmers in examining weather, soil, and field data to improve farming operations and crop productivity. AI in the agriculture market seems to be driven by the Internet of Things (IoT) due to its ability to revolutionize and transform current farming methods to a new level. Although, collecting accurate field data requires high initial investments which may hamper the growth of AI in the agriculture market. Some of the leading companies influencing the market are Ag Leader Technology, Trimble, Agribotix, Granular, SAP, Mavrx, PrecisionHawk, aWhere, IBM and Prospera Technologies.

CHICAGO – Agribusiness is increasingly turning to natural and sustainable alternatives to chemicals as consumers rebuff genetically modified foods and concerns grow over Big Ag's role in climate change. At the heart of the trend are innovations that harness beneficial microorganizms in the soil, including seed-coatings of naturally occurring bacteria and fungi that can do the same work as traditional chemicals, from warding off pests to helping plants flourish, according to a global patent study by research firm GreyB Services. Much of the research in crop biotech is centered in the United States, China, Germany, Japan and South Korea, according to the U.N. agency WIPO. "Both entrepreneurs and investors are saying, 'Hey, the writing is on the wall, we're entering a post-chemical world,'" said Rob LeClerc, chief executive officer of AgFunder, an online venture-capital platform. "The seed companies who have billions in market cap are like'We need to do something,' and everyone recognizes the opportunity."

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.