If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Deep learning offers the promise of bypassing the procedure of manual feature engineering by learning representations in conjunction with statistical models in an end-to-end fashion. In any case, neural network architectures themselves are ordinarily designed by specialists in a painstaking, ad hoc fashion. Neural architecture search (NAS) has been touted as the way ahead for lightening this agony via automatically identifying architectures that are better than hand-planned ones. Machine learning has given some huge achievements in diverse fields as of late. Areas like financial services, healthcare, retail, transportation, and more have been utilizing machine learning frameworks somehow, and the outcomes have been promising.
Variational autoencoders (VAE) are a powerful and widely-used class of models to learn complex data distributions in an unsupervised fashion. One important limitation of VAEs is the prior assumption that latent sample representations are independent and identically distributed. However, for many important datasets, such as time-series of images, this assumption is too strong: accounting for covariances between samples, such as those in time, can yield to a more appropriate model specification and improve performance in downstream tasks. In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue. The GPPVAE aims to combine the power of VAEs with the ability to model correlations afforded by GP priors.
Most robots lack the ability to learn new objects from past experiences. To migrate a robot to a new environment one must often completely re-generate the knowledge- base that it is running with. Since in open-ended domains the set of categories to be learned is not predefined, it is not feasible to assume that one can pre-program all object categories required by robots. Therefore, autonomous robots must have the ability to continuously execute learning and recognition in a concurrent and interleaved fashion. This paper proposes an open-ended 3D object recognition system which concurrently learns both the object categories and the statistical features for encoding objects.
The past decade has already forced a shift in the professional skills required of workers. New technologies like collaboration apps and document and knowledge capture tools have had a wide-ranging impact on what people can do: speeding up communication, enabling faster access to and dissemination of information, and multiplying reach. Yet even among all this progress, nothing promises to be more disruptive to the future of work than the introduction of artificial intelligence. Recent data from McKinsey suggests that almost every occupation will be touched by automation. But the firm forecasts that intelligent technology is likely to automate away just 5% of roles, meaning that most of us will live in a world where AI helps us by taking on just part of our current jobs.
From the ethical treatment of farm animals to sleep optimization and fashion, Zank Bennett, CEO of Bennett Data Science, helps entrepreneurs utilize artificial intelligence in a wide array of industries. Working with large and small companies alike, Bennett makes complicated technology easy-to-use so even entrepreneurs with little tech experience can harness the power of AI. I recently spoke with Bennett for more insight on how business can capitalize on data science and reap its rewards. Why should entrepreneurs utilize data science, even if their startups are not tech-focused? For companies to be successful nowadays, they really have to nail the personalization piece, and entrepreneurs get this more than most.
If you'd like to peer all of the code, here's the Github hyperlink. In this newsletter, I'll in brief pass over a easy strategy to code and teach a textual content technology fashion in Python the use of Keras and Tensorflow. Our purpose is to coach a fashion to emulate the talking taste of the textual content it's educated on. In this situation, our knowledge set is 7009 sentences from Edgar Allen Poe horror tales. The first step to coaching any NLP fashion is the tokenization of phrases.
This post was originally published on the Shoprunner Engineering blog here feel free to check it out and at some of the other work our teams are doing. Our ShopRunner Data Science team allows all members to have a quarterly hack week. It is important for data science teams to keep innovating so once per quarter team members are allowed to spend a week working on more speculative projects of their choice. For my 2019 Q3 hack week I decided to build a series of generator models to attempt to create fake products. Generator models are models commonly trained to create realistic images or text based on real world examples.
I'm often asked why I think Artificial-Intelligence (AI) tools are key for luxury brands' success in the 21st Century. I think that's because AI is one of the most overused buzzwords today, and many people use the term very loosely. In fact, most people discuss AI without really understanding it or what the benefits are. First off, one must know that AI is just a small part of what I call advanced data querying technologies. These technologies also include machine learning and advanced data analytics.
The first, Runway Palette, organizes the work of almost 1,000 different fashion designers by color. Google says it worked with a publication called Business of Fashion to create the app. With the help of BoF's photo collection, which includes more than 140,000 photos from some 4,000 fashion shows, Google used a machine-learning algorithm to organize all the photographed outfits by color. You can explore the archive in one of two ways. You can either tap through the very cool interactive visualization tool Google created, or you can photograph a piece of clothing and the app will show you similar looks.