The local authority, Council of Agriculture (COA), guided seeds manufacturers to improve F1 hybrid seed collecting techniques that resulted in superior harvest performance of cruciferous vegetables, watermelons and cherry tomatoes. The featured exhibitors invited to showcase at the pavilion of Taiwan Seed Varieties this year include Besgrow Seed, Ching Long Seed, Agronew Trading, Taiwan Banana Research Institute, Known-You Seed and Sing-Flow Seed Trading. One of the exhibitors - CH Biotech, developed'Weather Mend,' a product to regulate plant gene expression system with non-genetically modified active components, which is seen as a crucial solutions for crops in facing extreme climate change. This product aims to increase plant survival rate and helps steady food supply. Not only will it initiate plants' defence mechanism in advance, but also reduce losses caused by severe weather.

Westworld is an HBO television series based on American novelist Michael Crichton's 1973 futuristic science-fiction thriller about an android Western theme park. According to Variety, the first season of Westworld garnered an average of approximately 13.2 million viewers per episode. There are many reasons contributing to the show's popularity. On the surface level, the series is thrilling, complex, chilling, unpredictable, and completely unsettling – the very emblem of dystopian nightmares. On a deeper level, the show raises many philosophical questions on the potential impact of human-like robots powered by artificial intelligence (AI).

Yes, but it also groups different words that have the same base form. So biographies and texts about animals might be wrongly grouped together, introducing noise in the corpus. I suspect, depending on the language, that this can happen a lot (or not) and greatly influence the process. I know it helps in Finnish and French and doesn't help in Swedish (with the texts I've used; I have compared LDA output on lemmatised and non-lemmatised versions of the same corpus), I was wondering if you had experience with other languages?

Topic models have been widely explored as probabilistic generative models of documents. Traditional inference methods have sought closed-form derivations for updating the models, however as the expressiveness of these models grows, so does the difficulty of performing fast and accurate inference over their parameters. This paper presents alternative neural approaches to topic modelling by providing parameterisable distributions over topics which permit training by backpropagation in the framework of neural variational inference. In addition, with the help of a stick-breaking construction, we propose a recurrent network that is able to discover a notionally unbounded number of topics, analogous to Bayesian non-parametric topic models. Experimental results on the MXM Song Lyrics, 20NewsGroups and Reuters News datasets demonstrate the effectiveness and efficiency of these neural topic models.

Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse. We introduce a solution that enables agents to learn temporally extended actions at multiple levels of abstraction in a sample efficient and automated fashion. Our approach combines universal value functions and hindsight learning, allowing agents to learn policies belonging to different time scales in parallel. We show that our method significantly accelerates learning in a variety of discrete and continuous tasks.

Just over fifty years ago, a French journalist, Jean-Jacques Servan-Schreiber, published his book, Le Défi Américain (aka The American Challenge, 1967). It presented the United States and Europe as engaged in a silent economic war. In that war, he wrote, Europe was being completely outclassed on all fronts in dealing with the Third Industrial Revolution (electronics, information technology, and automation). The invading industrial armies of the day--1960s giants such as General Motors and IBM--were becoming dominant in Europe because of stronger and more flexible management techniques, technological tools, and research capacity. The book became an international hit, selling an unprecedented 600,000 copies in France alone.

A data scientist has designed an algorithm that predicts which Game of Thrones characters are most likely to die in season 8. The final season of Game of Thrones may be a year away, but a lot of fans are left wondering what to expect and more specifically, which characters are going to die. Leave it up to a numbers and science guy to figure that one out. Taylor Larkin, a data scientist at Boston-based DataRoot, went through an extensive Thrones wiki database to analyze the traits of nearly 2,000 characters. Using automated machine learning, Larkin didn't cut any corners and factored in everything from gender, age, house, family lineage and more.

A transparent skull allows people to literally peer into the head of Sophia, one of the most sophisticated humanoid robots yet built. Hong Kong firm Hanson Robotics created Sophia with an advanced neural network and delicate motor controls that allow the machine to emulate human social interactions. Rubberized faces stretch into familiar shapes, driven by tiny motors and a distant version of artificial intelligence--is this the future? Meet Sophia, a social robot created by former Disney Imagineer David Hanson. Modeled in part after Audrey Hepburn and Hanson's wife, the robot was built to mimic social behaviors and inspire feelings of love and compassion in humans.

The inputs were 96x96 images, and the target outputs were 30-value vectors indicating x,y pairs for 15 facial keypoints. We had to design a CNN from scratch to perform the task. My architecture was three convolutional layers, each followed by a max pooling layer with dropout, then a two-layer dense regression network at the end. Training was done on an EC2 p2xlarge GPU instance, and took around 10 minutes to perform 250 epochs (though there was a lot of trial and error so all told I spent a few hours on training different architectures). The dataset came from this Kaggle competition!

My initial uncharitable take was: College profs need to understand product development is harder than they think. A number of points here indicate a lack of progress on general AI by pointing to product decisions that would be present no matter the implementation. He seems to then use this as a rallying call to encourage going back to knowledge engineering circa late 80's early 90's? I'm going to guess i'm missing something. Yes general ai is far away.