Training an artificial intelligence agent to do something like navigate a complex 3D world is computationally expensive and time-consuming. In order to better create these potentially useful systems, Facebook engineers derived huge efficiency benefits from, essentially, leaving the slowest of the pack behind. It's part of the company's new focus on "embodied AI," meaning machine learning systems that interact intelligently with their surroundings. That could mean lots of things -- responding to a voice command using conversational context, for instance, but also more subtle things like a robot knowing it has entered the wrong room of a house. Exactly why Facebook is so interested in that I'll leave to your own speculation, but the fact is they've recruited and funded serious researchers to look into this and related domains of AI work.
After a school shooting in Parkland, Florida left 17 people dead, RealNetworks decided to make its facial recognition technology available for free to schools across the US and Canada. If school officials could detect strangers on their campuses, they might be able to stop shooters before they got to a classroom. Anxious to keep children safe from gun violence, thousands of schools reached out with interest in the technology. Dozens started using SAFR, RealNetworks' facial recognition technology. From working with schools, RealNetworks, the streaming media company, says it's learned an important lesson: Facial recognition isn't likely an effective tool for preventing shootings.
In the previous article, we studied the k-NN. One thing that I believe is that if we can correlate anything with us or our lives, there are greater chances of understanding the concept. So I will try to explain everything by relating it to humans. It tries to make the inter-cluster data points as similar as possible while also keeping the clusters as different or as far as possible. It assigns data points to a cluster such that the sum of the squared distance between the data points and the cluster's centroid is at the minimum.
I've always wondered if we could somehow use animals superior sense of vision to understand artificial neural networks better. Eagles have incredible spatial resolution, and understanding how they viewed adversarial samples might shed more light on the effect of spatial resolution for adversarial samples.
It's one thing to develop a working machine learning model, it's another to put it to work in an application. Cortex Labs is an early-stage startup with some open-source tooling designed to help data scientists take that last step. The company's founders were students at Berkeley when they observed that one of the problems around creating machine learning models was finding a way to deploy them. While there was a lot of open-source tooling available, data scientists are not experts in infrastructure. CEO Omer Spillinger says that infrastructure was something the four members of the founding team -- himself, CTO David Eliahu, head of engineering Vishal Bollu and head of growth Caleb Kaiser -- understood well.
Automated farming equipment has perhaps never been a hotter topic than right now. Adding fuel to the fire, farm equipment giant John Deere had a big splash at last week's Consumer Electronics Show (CES) in Las Vegas, NV. Last year was a tough act to follow. In 2019, it exhibited its machine learning (ML) and artificial intelligence (AI) enabled S-Series combine. This year, Deere brought out the big guns with its R4038 sprayer.
For embedded enthusiasts, we are offering a focused agenda at GTC 2020. Be the first to learn about our newest AI products and developer tools at NVIDIA Jetson Developer Days. With sessions and tutorials for all experience levels, this is the perfect place to learn more about AI and its applications.
AI and Big Data in Cancer: From Innovation to Impact, a new conference from Elsevier, a global information and analytics business specializing in science and health, will bring together experts from all aspects of cancer research and the digital medicine value chain to understand how to translate artificial intelligence and data-driven innovations into new clinical care practices for patients. These leaders, including 2018 Nobel laureate for Medicine, Dr. James Allison, will share pragmatic insights on finding the right partners to move innovations successfully forward. "It is time to shift our conversation from'what-technology-can-do' to'what-medicine-needs' and to raise awareness of what else is necessary to translate an AI-enabled and data-driven innovation into a marketed product," said Dr. Lynda Chin, Conference Chair, Founder and CEO of Apricity Health and Professor at Dell Medical School at the University of Texas, USA. "Understanding what these hurdles are is the first step to overcoming them. "The aim of this conference is to bring innovators together with stakeholders, from patients, clinicians and developers to regulators, payers and investors, so they can network and identify collaborators who can help them accelerate the translation of their innovation into clinical practices," Dr. Chin said. "Insights from the program's 40 key opinion leaders will advance the emerging digital medicine industry, building bridges from computer to clinics," said Laura Colantoni, Vice President for Reference Content, Elsevier, and one of the main organizers for the conference. "We are particularly excited about establishing this conference as a venue for successful innovators, influential facilitators, regulators and payers, as well as investors to find, engage and collaborate with clinicians, researchers and patients to accelerate progress in this area.
Products we use every day now have become increasingly software driven, connected and sophisticated. So your engineering process has become exponentially more complex. Consider the automobile, for example. Today's automobiles require millions of lines of software to operate critical systems like braking, engine performance and collision avoidance. The implementation of more requirements, more dependency on modelling, greater testing as well as increasing global collaboration between teams to cope with customer demands have created new challenges.
Global consulting firm Accenture's'AI: Built to Scale' study said companies are using AI to enhance customer service, productivity, brand perception, working capital utilization and to plan new product offerings. For the study, Accenture surveyed 1,500 C-suite executives from companies with a minimum revenue of $1 billion in 12 countries, across 16 industries. Although a majority of companies surveyed are still stuck at conducting AI experiments, a small but growing group of organisations across the globe have successfully used AI to improve efficiency. A major North American technology company required a dynamic integrated approach for its vast and complex supply chain. Open source machine learning (ML) tools were used to analyse demand uncertainty, cost drivers, customer relationships, and rate of technological change.