Vulnerable elephant populations are now being tracked from space using Earth-observation satellites and a type of artificial intelligence (AI) called machine learning. As part of an international project, researchers are using satellite images processed with computer algorithms, which are trained with more than 1,000 images of elephants to help spot the creatures. With machine learning, the algorithms can count elephants even on'complex geographical landscapes', such as those dotted with trees and shrubs. Researchers say this method is a promising new tool for surveying endangered wildlife and can detect animals with the same accuracy as humans. Elephants in woodland as seen from space.

Last year, we identified blockchain, cloud, open-source, artificial intelligence, and knowledge graphs as the five key technological drivers for the 2020s. Although we did not anticipate the kind of year that 2020 would turn out to be, it looks like our predictions may not have been entirely off track. Let's pick up from where we left off, retracing developments in key technologies for the 2020s: Artificial intelligence and knowledge graphs, plus an honorable mention to COVID-19-related technological developments. This TechRepublic Premium ebook compiles the latest on cancelled conferences, cybersecurity attacks, remote work tips, and the impact this pandemic is having on the tech industry. In our opener for the 2020s, we laid the groundwork to evaluate the array of technologies under the umbrella term "artificial intelligence."

This observation--that to understand Proust's text requires knowledge of various kinds--is not a new one. We came across it before, in the context of the Cyc project. Remember that Cyc was supposed to be given knowledge corresponding to the whole of consensus reality, and the Cyc hypothesis was that this would yield human-level general intelligence. Researchers in knowledge-based AI would be keen for me to point out to you that, decades ago, they anticipated exactly this issue. But it is not obvious that just continuing to refine deep learning techniques will address this problem.

Last week, the U.S. Food and Drug Administration presented the organization's first Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan. This plan portrays a multi-pronged way to deal with the Agency's oversight of AI/ML-based medical software. The Artificial Intelligence/Machine Learning (AI/ML)- Based Software as a Medical Device (SaMD) Action Plan is a response to stakeholder input on the FDA's 2019 regulatory structure for AI and ML-based medical items. FDA additionally will hold a public workshop on algorithm transparency and draw in its stakeholders and partners on other key activities, for example, assessing predisposition in algorithms. While the Action Plan proposes a guide for propelling a regulatory framework, an operational structure gives off an impression of being further down the road.

I am a recent graduate of the Galvanize Data Science Immersive Bootcamp. In this Data Science Bootcamp we spent 3 months learning Statistics, Linear Algebra, Calculus, Machine Learning, SQL, and Python Programming. The San Francisco based program I attended was transferred from in-person to remote due to the COVID-19 pandemic. To say this experience was challenging would be an understatement. My official day at the Bootcamp started at 8:30 AM and ended at 8:30 PM Monday through Friday.

As someone who has interviewed with several companies for Data Scientist positions, as well as someone who has searched and explored countless required qualifications for interviews, I have compiled my top five Data Science qualifications. These qualifications are not only expected to be required by the time of interview, but also just important qualifications to keep in mind at your current work, even if you are not interviewing. Data Science is always evolving so it is critical to be aware of new technologies within the field. These requirements may differ from your personal experiences, so keep in mind this article is stemming from my opinion as a professional Data Scientist. These qualifications will be described as key skills, concepts, and various experiences that are expected to have before entering the new role or current role.

Google's Google Cloud division today announced it has made generally available two search functions that rely on machine learning techniques to help retailers who use its cloud service. Called Vision API product search and Recommendations AI, the two services are part of what Google has unveiled as a suit of functions called Product Discovery Solutions for Retail. The vision search function will let a retailer's customers submit a picture and received ranked results of products that match the picture in either appearance or semantic similarity to the object. Recommendations, said Google, is "able to piece together the history of a customer's shopping journey and serve them with customized product recommendations." Both are generally available now to retailers.

People tend to make snap judgments on each other in a single look and now an algorithm claims to have the same ability to determine trustworthiness for obtaining a loan in just two minutes. Tokyo-based DeepScore unveiled its facial and voice recognition app last week at the Consumer Electronics Show that is touted as a'next-generation scoring engine' for loan lenders, insurance companies and other financial institutions. While a customer answers 10 question, the AI analyzes their face and voice to calculate a'True Score' that can be help companies with the decision to deny or approve. DeepScore says its AI can determine lies with 70 percent accuracy and a 30 percent false negative rate, and will alert companies that fees need to be increased if dishonesty is detected. However, scientists raise concerns about bias saying the app is likely to discriminate against people with tics or anxiety, resulting in these individuals not receiving necessary funds or coverage, Motherboard reports.

For the past 15 years, NASA's Mars Reconnaissance Orbiter has been doing laps around the Red Planet studying its climate and geology. Each day, the orbiter sends back a treasure trove of images and other sensor data that NASA scientists have used to scout for safe landing sites for rovers and to understand the distribution of water ice on the planet. Of particular interest to scientists are the orbiter's crater photos, which can provide a window into the planet's deep history. NASA engineers are still working on a mission to return samples from Mars; without the rocks that will help them calibrate remote satellite data with conditions on the surface, they must do a lot of educated guesswork when it comes to determining each crater's age and composition. For now, they need other ways to tease out that information.

We basically train machines so as to include some kind of automation in it. In machine learning, we use various kinds of algorithms to allow machines to learn the relationships within the data provided and make predictions using them. So, the kind of model prediction where we need the predicted output is a continuous numerical value, it is called a regression problem. Regression analysis convolves around simple algorithms, which are often used in finance, investing, and others, and establishes the relationship between a single dependent variable dependent on several independent ones. For example, predicting house price or salary of an employee, etc are the most common regression problems.