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) …
The work of MIT laptop scientist Aleksander Madry is fueled by one core mission: "doing machine studying the proper approach." In his classroom and past, he additionally worries about questions of moral computing; as we strategy an age, the place synthetic intelligence can have a nice impression on many sectors of society. Curiously, his work with machine studying dates again solely a few years, shortly after he joined MIT in 2015. At that point, his analysis group has revealed a number of vital papers demonstrating that sure fashions will be simply tricked to supply inaccurate results -- and displaying the best way to make them extra strong. In the long run, he goals to make every model's decisions extra interpretable by people, so researchers can peer inside to see the place issues went awry.
With headlines everywhere focusing on disposable plastics and air travel emissions, it's clear that our individual, everyday purchasing choices--from what we eat to how we get around--impact the world around us. But how about what we wear? According to the UN Alliance for Sustainable Fashion, apparel manufacturing produces 20% of the world's water waste and up to 10% of its carbon output and sends more than 21 billion tons of textiles to landfills each year. But it's also a $2.4 trillion dollar industry that employs more than 60 million people worldwide. Considering this scale and impact, the industry is at a crossroads, devising disruptive technologies, rethinking business models, and searching for innovation at every step -- design, production, distribution, and reuse.
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 superpowers of technology are unimaginable, and there's no stopping building fashion businesses around it. Digital supermodels, virtual trial rooms and analytics could possibly change the way we shop in the future. In the age of artificial intelligence, analytics and big data--which collectively translate unstructured data to meaningful solutions, trends and patterns--how does the fashion industry keep up? For instance, excess stock arises from lack of planning and knowledge. With data, we can see minute-by-minute how stock moves and thus, eliminate errors.
Entrepreneur and investor Peter Diamandis predicts that the future of shopping will be "always on", thanks to ubiquitous augmented reality. Artificial intelligence is in position to streamline and personalise the process, while virtual reality shopping can be successful if it creates a more social experience. Brands should prepare for far more data collection by asking the right questions and using AI to correlate more details. SAN FRANCISCO-- Here's the future of shopping, as Silicon Valley entrepreneur and investor Peter Diamandis sees it: augmented reality glasses will present an "always-on" shopping mode, artificially intelligent digital assistants will know your taste better than you and clothing will be made exactly to your measurements. And it could happen faster than one might think, he says.
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.
Express Wavenet is an improved optical diffractive neural network. At each layer, it uses wavelet-like pattern to modulate the phase of optical waves. For input image with n2 pixels, express wavenet reduce parameter number from O(n2) to O(n). Only need one percent of the parameters, and the accuracy is still very high. In the MNIST dataset, it only needs 1229 parameters to get accuracy of 92%, while the standard optical network needs 125440 parameters. The random shift wavelets show the characteristics of optical network more vividly. Especially the vanishing gradient phenomenon in the training process. We present a modified expressway structure for this problem. Experiments verified the effect of random shift wavelet and expressway structure. Our work shows optical diffractive network would use much fewer parameters than other neural networks. The source codes are available at https://github.com/closest-git/ONNet.
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.