Health care workers are not the only unwilling essential services frontline workers at increased risk of COVID-19. According to the Washington Post on April 12, "At least 41 grocery workers have died of the coronavirus and thousands more have tested positive in recent weeks". At the same time, grocery stores are seeing a surge in demand and are currently hiring. The food industry is also seeing increasing adoption of robots in both the back end supply chain and in the food retail and food service sectors. "Grocery workers are risking their safety, often for poverty-level wages, so the rest of us can shelter in place," said John Logan, director of labor and employment studies at San Francisco State University. "The only way the rest of us are able to stay home is because they're willing to go to work."
Artificial Intelligence and Machine Learning solutions offer large possibilities to optimize and automate processes, save costs and make less human error possible for many industries. Food and Beverage is not an exception, where it can be beneficially applied in restaurants, bar and cafe businesses as well as in food manufacturing. These two segments have common use cases where AI in the food industry can be applied, as well as different ones, which is linked to different problems that must be solved. Knowing what goods to manufacture in large amounts or what dishes are the best choice to include in your restaurant menu is the key to increase earnings. Often customers' and market demands are changing very fast and so it is even more important to be one step ahead to take measures in time.
We propose a novel neural topic model in the Wasserstein autoencoders (W AE) framework. Unlike existing variational autoencoder based models, we directly enforce Dirichlet prior on the latent document-topic vectors. We exploit the structure of the latent space and apply a suitable kernel in minimizing the Maximum Mean Discrepancy (MMD) to perform distribution matching. We discover that MMD performs much better than the Generative Adversarial Network (GAN) in matching high dimensional Dirichlet distribution. We further discover that incorporating randomness in the encoder output during training leads to significantly more coherent topics. To measure the diversity of the produced topics, we propose a simple topic uniqueness metric. Together with the widely used coherence measure NPMI, we offer a more wholistic evaluation of topic quality. Experiments on several real datasets show that our model produces significantly better topics than existing topic models.
If you were dreaming of having your next grande no-whip soy latte delivered by drone, you can forget about it. Project Wing's wings were clipped by Google parent Alphabet as it tightens budgets across the board, Bloomberg reported Tuesday, quoting people familiar with the decision. Bloomberg said the decision to end the proposed venture with Starbucks followed the departure of project leader Dave Vos, who has not been replaced. Hiring also was frozen, and some people were urged to seek employment elsewhere in the company, Bloomberg reported. The Alphabet decision comes as other companies are ramping up drone programs despite a lack of Federal Aviation Administration approval for deliveries outside test zones.