Collaborating Authors

New drone attack AI tech tracks 'out of view' targets

FOX News

Fox News Flash top headlines are here. Check out what's clicking on What if a U.S. drone was closely tracking an armed enemy vehicle as it transits rough terrain, enters urban areas and comes closer to vulnerable target areas when, all of a sudden, the target leaves a sensor's field of view, becoming seemingly un-trackable? Not so fast, according to emerging AI-enabled tracking technology now being developed by CACI, a technology firm supporting the U.S. military. Fast-maturing algorithms are now able to analyze a host of variables at one time, at lightning speed, to discern a target's trajectory and continue tracking an object even after it has left a sensor's field of view.

K-Nearest Neighbors (KNN) Algorithm In Machine Learning


K-Nearest Neighbors (KNN) Algoritm in Machine Learning will help you to master all the concepts of KNN. KNN algorithm is one of the simplest, non-parametric, lazy classification learning algorithm. Its purpose is to use a dataset in which the data points are separated into several classes to predict the classification of a new sample point. This tutorial on "KNN Algoritm in Machine Learning" will help you to master all the concepts of K-nearest neighbors. Its purpose is to use a dataset in which the data points are separated into several classes to predict the classification of a new sample point.

Statistics and Machine Learning -- When to Use What?


We live in a world of data explosion where computers are like a commodity, that's why associating almost every problem with trending tech buzzwords like "artificial intelligence", "machine learning", and "deep learning" seems like an avant-garde thing to do. It is almost intuitive and convenient to do so given readily available software and programming libraries on the internet. The most daunting part is probably to pick the suitable one and feed it with your data, then voilà -- here are the results. A search on Google with "machine learning models" swiftly returns you more than 700 million results in less than a second, just to show you how easy it is to gather on the magnitude of availability for ML models but how difficult it is to actually decide on the one that suits you. And then here come the million-dollar questions-- Am I actually selecting the best ML for my use case?

Data Science vs Machine Learning. Here's the Difference.


At first, perhaps data science and machine learning could be seen as interchangeable titles and fields; however, with a closer look, we realize machine learning is more-so a combination of software engineering and data engineering than data science. Below, I will outline where the fields do and do not cross over.

Top 10 robotics startups to keep an eye on in 2020


Running a robotics startup is no easy task. Yet, we are always amazed by the number of robotics startups working on innovative technologies. Here, in alphabetical order, are 10 robotics startups The Robot Report will be watching in 2020. The companies are working on a variety of products, including autonomous vehicles, mobile robots for construction, toy robots, and software to give robots common sense and make them easier to use. It's hard to narrow this list down to just 10 robotics startups, so please share in the comments some robotics startups you will be watching in 2020. Make sure to also check out our must-watch robotics startups from 2019.

The Birthplace of Artificial Intelligence?


International communities of invention and innovation, IFIP WG 9.7 International conference on the history of computing, HC 2016, Brooklyn, New York, May 25–29 2016, revised selected papers, Springer International Publishing AG Switzerland, Cham 2016, pages 181–185

CFPB Highlights the Growing Role of Artificial Intelligence in the Delivery of Financial Services


The Consumer Financial Protection Bureau ("CFPB") has published guidance on July 7, 2020 which highlights the potential use of Artificial Intelligence ("AI") in the delivery of financial services--particularly in credit underwriting models. In addition to providing an overview of the ways in which AI is being used by financial institutions, the publication addresses: (1) industry uncertainty about how AI fits into the existing regulatory framework, especially for credit underwriting; and (2) the tools that the CFPB has been using to promote innovation, facilitate compliance, and reduce regulatory uncertainty. As the publication notes, financial institutions are starting to deploy AI across a range of functions, including as virtual assistants that can fulfill customer requests, in models to detect fraud or other potential illegal activity, or as compliance monitoring tools. Credit underwriting is one specific area in which AI may have a profound impact. Credit underwriting models that are built upon AI have the potential to expand credit access by permitting lenders to evaluate creditworthiness of some of the millions of consumers who are "unscorable" using traditional underwriting systems.

Global Big Data Conference


ModiHost is a new platform for hotels that uses artificial intelligence to offer a better hotel management system, centered around personalization of the guest experience. In turn they aim to drive increased spending and brand loyalty. They say they've cracked the code that many hotels haven't, offering a solution for remembering guest preferences and anticipating their needs that most hotels wouldn't be able to employ on their own. As the company says it in its whitepaper: "Hotel management is a complex and convoluted industry. It is also a highly inefficient one. The need to operate multiple systems, integrate different booking systems, and process reservations via mediums ranging from email to fax, have made hotel management hopelessly complicated."

White House advisory council calls on U.S. to increase AI funding to $10 billion by 2030


Earlier this week, the President's Council of Advisors on Science and Technology (PCAST) released a report outlining what it believes must happen for the U.S. to advance "industries of the future." Several of the committee's suggestions touched on the field of AI as it relates to federal, state, and private-sector partnerships, as well as departmental budgetary considerations. In particular, the report recommends that the U.S. grow nondefense federal investments in AI by 10 times over the next 10 years and for the federal government to create national AI "testbeds," expanding the National Science Foundation's (NSF) AI Institutes with at least one AI Institute in each state and creating a "National AI Consortia" to share capabilities, data, and resources. Loosely, PCAST -- which lives in the Office of Science and Technology -- provides advice to the president on science and technology policy. In the report, the committee argues the U.S. will need to boost AI R&D investments from $1 billion a year in 2020 to $10 billion a year by 2030 in order to remain competitive.

Lytics AI Models Boldly Promise Reduction In Time To Performance


Lytics, a customer data platform (CDP), is offering a set of new marketing tools inside Lytics View that provides recommendations for improving performance based on artificial intelligence in as little as seven days. Seven days may seem like a long time when considering that the internet provides instant gratification for most services, but traditional CDP platforms historically can take anywhere from three to five months, up to multiple years to get up and running, says Lytics President Jascha Kaykas-Wolff joined the company about five months ago after serving as CMO at Mozilla. A few companies have been testing the tool in the past month. The idea is to improve performance in less than 30 days. "It's a fairly bold promise," Kaykas-Wolff said.