Monitoring whales in remote areas is important for their conservation; however, using traditional survey platforms (boat and plane) in such regions is logistically difficult. The use of very high-resolution satellite imagery to survey whales, particularly in remote locations, is gaining interest and momentum. However, the development of this emerging technology relies on accurate automated systems to detect whales, which are currently lacking. Such detection systems require access to an open source library containing examples of whales annotated in satellite images to train and test automatic detection systems. Here we present a dataset of 633 annotated whale objects, created by surveying 6,300 km2 of satellite imagery captured by various very high-resolution satellites (i.e. WorldView-3, WorldView-2, GeoEye-1 and Quickbird-2) in various regions across the globe (e.g. Argentina, New Zealand, South Africa, United States, Mexico). The dataset covers four different species: southern right whale (Eubalaena glacialis), humpback whale (Megaptera novaeangliae), fin whale (Balaenoptera physalus), and grey whale (Eschrichtius robustus).

In brief Miscreants can easily steal someone else's identity by tricking live facial recognition software using deepfakes, according to a new report. Sensity AI, a startup focused on tackling identity fraud, carried out a series of pretend attacks. Engineers scanned the image of someone from an ID card, and mapped their likeness onto another person's face. Sensity then tested whether they could breach live facial recognition systems by tricking them into believing the pretend attacker is a real user. So-called "liveness tests" try to authenticate identities in real-time, relying on images or video streams from cameras like face recognition used to unlock mobile phones, for example.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Some say, "data is the new oil," with an air of seriousness. And while the phrase may capture a certain truth about the modern digital economy, it fails to model the way that bits can be copied again and again. Sometimes the ease of sharing creates a distinct absence of scarcity and that changes the economics of the entire game. One of the best ways to visualize this is to tap into some open source datasets that are proliferating on the Internet.

In two previous articles I covered two of the most basic models used in machine learning -- linear regression and logistic regression. In both cases, we were interested in searching for the set of model parameters m that result in the best model predictions d' of the observed targets d, and in both cases this was done by minimizing some loss function L(m), which measures the error between d' and d. A good proportion of machine learning -- from simple linear regression to deep learning models, essentially involves the minimization of some sort of loss function -- and yet, many data science or machine learning books/tutorials/materials tend to place more emphasis on the model itself than on the loss function! In this article, we will continue on where we left off from the previous two articles and focus on loss functions before exploring more advanced models in future articles! Now, just as "best" is a very subjective word, so are loss functions!

Edge Machine Learning (Edge ML) is one of the most talked-about tech advancements since the Internet of Things (IoT), and for a good reason. With the rise of IoT came an explosion of Smart Devices connected to the Cloud, but the network was not yet ready to support this surge in demand. Cloud networks were congested, and companies overlooked key issues with Cloud computing, such as security. So, what is Edge ML anyway? Edge ML is a technique by which Smart Devices can process data locally (either using local servers or at the device-level) using machine and deep learning algorithms, reducing reliance on Cloud networks.

Technology continues to advance at impressive rates. And while the novelty of technologies such as self-parking cars and robotic vacuums have worn off, we are still many years away from the age of computers capable of human thought. Well, that was true before the development of Artificial Neural Networks (ANN), of course. ANN is one of the only techniques currently available for training machines to truly think like people, and it is a tool used within the deep learning space. Artificial intelligence, defined broadly, is the field of training machines to autonomously perform tasks normally thought to require intelligence. Beneath that umbrella is machine learning, in which machines autonomously learn new tasks, and deep learning is a further subcategory of machine learning.

THE FUNDAMENTAL assumption of the computing industry is that number-crunching gets cheaper all the time. Moore's law, the industry's master metronome, predicts that the number of components that can be squeezed onto a microchip of a given size--and thus, loosely, the amount of computational power available at a given cost--doubles every two years. For many comparatively simple AI applications, that means that the cost of training a computer is falling, says Christopher Manning, an associate director of the Institute for Human-Centered AI at the University of Stanford. But that is not true everywhere. A combination of ballooning complexity and competition means costs at the cutting edge are rising sharply.

Advancements in the power of machine learning have brought with them major data privacy concerns. This is especially true when it comes to training machine learning models with data obtained from the interaction of users with devices such as smartphones. So the big question is, how do we train and improve these on-device machine learning models without sharing personally-identifiable data? That is the question that we'll seek to answer in this look at a technique known as federated learning. The traditional process for training a machine learning model involves uploading data to a server and using that to train models.

Jan Bosch is a research center director, professor, consultant and angel investor in start-ups. You can contact him at jan@janbosch.com. In recent columns, I've been sharing my view on the quality of the data that many companies have in their data warehouses, lakes or swamps. In my experience, most of the data that companies have stored so carefully is useless and will never generate any value for the company. The data that actually is potentially useful tends to require vast amounts of preprocessing before it can be used for machine learning, for example.

As I sat down to write the predecessor to this article I has no idea that I would possibly turn these into a series - I don't know if any will follow this but maybe, just maybe I will have some ideas for engaging (hopefully) and useful content in the coming weeks/months!! However, I didn't really drill into specific hardware/infrastructure hazards. What does it look like? Developer PCs - A PC - Has a Graphics card (GPU) installed, fast processor, plenty of memory and some high-speed cache drives (SSD) to enable smaller datasets to be processed on the system for Proof of concept (POC) work whilst minimising the initial outlay.r Training machines - If you don't want to make the dev machines'heavy lifters' you'll need to invest in dedicated machines to handle the training workloads.