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) …
Facebook isn't entirely shying away from facial recognition, it seems. Code explorer Jane Manchun Wong has discovered a reference to a purported facial recognition system in Facebook's mobile app that would verify your identity. You'd have to take a "video selfie" where you look in different directions to give Facebook a more complete view of your face. It would bit like Apple's Face ID and similar systems, but there's no evidence it would require a depth sensor. Facebook vows that "no one else" will see the video and that it'll delete the clip after 30 days, although that's not quite as secure as systems like Face ID (which doesn't allow data to leave the device, and only captures "mathematical representations" of your face).
Machine learning has become a vital component to get solutions in everyday life. It is adding intelligence in every product we are using today. Marketing software and demand forecasting are using ML to a great extent. In the latest generation, the data is available in bulk, but we need more tools to handle this data. Machine learning is the only solution to so this task as it allows the computer to learn from data for improved analysis.
As Industry 4.0 continues to generate media attention, many companies are struggling with the realities of AI implementation. Indeed, the benefits of predictive maintenance such as helping determine the condition of equipment and predicting when maintenance should be performed, are extremely strategic. Needless to say that the implementation of ML-based solutions can lead to major cost savings, higher predictability, and the increased availability of the systems. After different ML projects, I wanted to write this article to share my experience and maybe help some of you integrate Machine Learning with predictive maintenance. What is predictive maintenance: In predictive maintenance scenarios, data is collected over time to monitor the state of equipment.
Imagine having a data collection of hundreds of thousands to millions of images without any metadata describing the content of each image. How can we build a system that is able to find a sub-set of those images that best answer a user's search query? What we will basically need is a search engine that is able to rank image results given how well they correspond to the search query, which can be either expressed in a natural language or by another query image. The way we will solve the problem in this post is by training a deep neural model that learns a fixed length representation (or embedding) of any input image and text and makes it so those representations are close in the euclidean space if the pairs text-image or image-image are "similar". I could not find a data-set of search result ranking that is big enough but I was able to get this data-set: http://jmcauley.ucsd.edu/data/amazon/
Technological Singularity is coming in 2040. Should we be scared of Artificial Intelligence? He did his PhD in Paris at Universite Pierre et Marie Curie, then became a Research Fellow and a lecturer at the University of Oxford. After returning to Poland, he took up research on artificial intelligence and mathematics, and founded a technological group ulam.ai, Within the group he co-founded multiple AI ventures ranging from logistics to the fashion market, and using cutting-edge technologies.
Robert Bosch is a world-class manufacturing and engineering company with over 200 plants and thousands of assembly lines world-wide. We rely on data for every aspect of our operations and we collect a lot of it. Our team applies machine learning to solve challenging problems in a wide variety of Bosch domains, including: manufacturing, engineering, supply chain & logistics, and internet of things. We are looking for a talented engineer who is passionate about building and deploying machine learning systems in production. Your work will have global impact by improving the quality and value of Bosch products.
A huge amount of the data collected today is made up of images and videos. That is why effective image processing for translating and obtaining information is crucial for businesses. Data scientists usually preprocess the images before feeding it to machine learning models to achieve desired results. Consequently, it is paramount to understand the capabilities of various image processing libraries to streamline their workflows. Scikit-image uses NumPy arrays as image objects by transforming the original pictures.
Zero-shot learning (ZSL) aims at understanding unseen categories with no training examples from class-level descriptions. To improve the discriminative power of zero-shot learning, we model the visual learning process of unseen categories with inspiration from the psychology of human creativity for producing novel art. We relate ZSL to human creativity by observing that zero-shot learning is about recognizing the unseen and creativity is about creating a likable unseen. We introduce a learning signal inspired by creativity literature that explores the unseen space with hallucinated class-descriptions and encourages careful deviation of their visual feature generations from seen classes while allowing knowledge transfer from seen to unseen classes. With hundreds of thousands of object categories in the real world and countless undiscovered species, it becomes unfeasible to maintain hundreds of examples per class to fuel the training needs of most existing recognition systems.
Apple has long been known for privacy on its computing and services platforms, but one area where the company has fallen short is Siri. Unfortunately, due to the way Siri works, recorded conversations with the virtual assistant can be used anonymously to verify that voice recognition is working properly. A human quality assurance engineer will listen to the recording to ensure the transcription to the Siri service is accurate. Apple came under fire for this practice earlier this year by not giving users the ability to opt out of this feature. While the feature is used for improving the quality of Siri, and the recordings are anonymized and reviewers won't know who originated the recording, users utilize Siri for calling, messaging, and looking up personal or private information, which can be a privacy concern for many people.