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) …
Using precise technology, this new driver-powered approach to safety is a simple, but powerful way for drivers to be more proactive and accountable for their own improvement, while giving management the necessary visibility and data to effectively monitor and intervene if needed. With these new MV AI-powered updates, when an event is detected, the DriveCam Event Recorder will issue a real-time in-cab alert to help drivers recognize and address their own risky behaviors and self-correct in the moment. Depending on the behavior, the alert will include a light and/or spoken phrase. With Lytx's ability to detect more than 60 driving behaviors with greater than 95% accuracy, its in-cab alerts are some of the most precise in the industry. Drivers will also have access to new check-in tools allowing them to review their own video and performance after-the-fact, including behavior duration.
More than 18 million new cancer cases are diagnosed globally each year, and radiotherapy is one of the most common cancer treatments--used to treat over half of cancers in the United States. But planning for a course of radiotherapy treatment is often a time-consuming and manual process for clinicians. The most labor-intensive step in planning is a technique called "contouring" which involves segmenting both the areas of cancer and nearby healthy tissues that are susceptible to radiation damage during treatment. Clinicians have to painstakingly draw lines around sensitive organs on scans--a time-intensive process that can take up to seven hours for a single patient. Technology has the potential to augment the work of doctors and other care providers, like the specialists who plan radiotherapy treatment.
If this article caught your eye, you probably have an interest in indexing or in online historical records. Maybe you've made indexing a part of your weekly or monthly volunteer efforts. If so, keep up the amazing work! You're making it possible for people around the world to discover their ancestors and learn more about their family histories. Still, our indexing volunteers have a colossal task in front of them.
Reinforcement Learning is a subset of machine learning. It enables an agent to learn through the consequences of actions in a specific environment. It can be used to teach a robot new tricks, for example. Reinforcement learning is a behavioral learning model where the algorithm provides data analysis feedback, directing the user to the best result. It differs from other forms of supervised learning because the sample data set does not train the machine.
Hosted by Dylan Doyle-Burke and Jessie J Smith, Radical AI is a podcast featuring the voices of the future in the field of artificial intelligence ethics. In this episode Jess and Dylan chat to Liz O'Sullivan about the state of surveillance in the world today. What should you know about the state of surveillance in the world today? What can we do as consumers to stop unintentionally contributing to surveillance? The facial recognition industry had a reckoning after the murder of George Floyd – are things getting better?
And what does the future hold for eNASCAR and the sport's overall sim racing ambitions? Officials like Clark and Myers suggest that a host of options are on the table: increased prize pools, additional TV coverage and more involvement from real-life, professional NASCAR drivers. Parker Kligerman, part owner of eNASCAR team Burton Kligerman eSports and a racing analyst for NBC Sports Network, said he's pushing his drivers to be more visible, streaming more frequently and creating digital content. "We have the opportunity to do things real-world racing can't," Kligerman said. "I continue to believe that there's huge potential here, and we've only just hit the tip of the iceberg."
It's nearly the end of the year, which means it's time to take a step back and reflect on 2020. Despite how much this year was an Annus Horribilis on so many levels, some very good technology products were released. While we cannot list them all, here's what made the top of our list on Jason Squared at ZDNet. In work and play, do you always give it your best? Then you probably want to give the best gifts, too, right?
Retailers can differentiate themselves and win over undecided shoppers. The holiday season is usually all-hands-on-deck under normal circumstances, but brands and their marketers are feeling the exhaustion and whiplash just as much as consumers this year. From lockdowns, store closures, reopenings and back to lockdowns once again, it's no surprise that the holiday shopping season has been added to the list of unprecedented events in 2020. Though it will come and go like last year, holiday shopping will most likely have no resemblance to past seasons. This will force many businesses to recalibrate their processes and reconsider fundamental strategies to keep their companies afloat.
Deep learning pioneer Yoshua Bengio has provocative ideas about the future of AI. For the first part of this article series, see here. It has only been 8 years since the modern era of deep learning began at the 2012 ImageNet competition. Progress in the field since then has been breathtaking and relentless. If anything, this breakneck pace is only accelerating. Five years from now, the field of AI will look very different than it does today.
Let's start with the one minute version: I was part of the EF12 London cohort in 2019, where I met my co-founder. A privacy-preserving medical-data marketplace and AI platform built around federated deep learning. The purpose of the platform would have been to allow data scientists to train deep learning models on highly sensitive healthcare data without that data ever leaving the hospitals. At the same time, thanks to a novel data monetization strategy and marketplace component, hospitals would have been empowered to make money from the data they are generating. We received pre-seed funding, valued at $1 million. Then the race for demo day began with frantic product building and non-stop business development.