How would you utilize SparkFun's RedBoard Artemis ATP, Qwiic components, and machine learning? We're looking for the next great group of projects to show how these three items can be incorporated into everyday life. Enter a project into one of four categories: Business, Home, Nature, and Wildcard. The board features the Artemis module, which allows for low-power machine learning and is compatible with Tensor Flow. Additionally, the Artemis module is powered by the Apollo3 mircoprocessor.

A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it. The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discover...

A new technology using artificial intelligence detects depressive language in social media posts more accurately than current systems and uses less data to do it. The technology, which was presented during the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases, is the first of its kind to show that, to more accurately detect depressive language, small, high-quality data sets can be applied to deep learning, a commonly used AI approach that is typically data intensive. Previous psycholinguistic research has shown that the words we use in interaction with others on a daily basis are a good indicator of our mental and emotional state. Past attempts to apply deep learning techniques to detect and monitor depression in social media posts have been shown to be tedious and expensive, explained Nawshad Farruque, a University of Alberta PhD student in computing science who is leading the new study. He explained that a Twitter post saying that somebody is depressed because Netflix is down isn't really expressing depression, so someone would need to "explain" this to the algorithm.

Chatbots have emerged as a great option for providing a 24/7 self-service solution to address a host of customer support requirements. They enable customers to get their questions answered in real-time and they free up support staff from having to field high volumes of repetitive inquiries. And with advances in artificial intelligence and machine learning, chatbots are becoming extremely effective at providing a cognitive and conversational experience that your customers will love. As chatbot adoption has increased, various types have entered the market to address different requirements. For example, chatbots built for specific B2C or B2B support use cases, called transactional bots, are very different from those built for more wide-ranging applications, referred to as knowledge bots.

It is truly amazing how many inspiring individuals have applied to our Collaborative AI Challenges. I am very honored to share the story of Anam today from whom I learned a lot by just speaking and listening to her. She will be part of our Global AI Challenge on Building a Machine Learning Model for PTSD treatment. If you are an organization that is eager to solve an impactful problem together with our community, you can apply here. Anam, tell us your story.

Continuously changing and evolving audience preferences alongside supporting technology innovations have been driving the TV industry from static a "one size fits all" offering towards more interactive and personalized experiences. Mobile and digital assistants, artificial intelligence (AI), and the Internet of Things (IoT) are influencing this shift in content consumption habits, hence building agile and scalable Cloud TV platforms is essential not only to expand market share and increase revenue, but also to mitigate churn in this crowded and highly competitive TV service marketplace. In the age where digital-first channels are becoming the primary audience engagement outlet, the amount of data is rising exponentially. How can TV service operators stay ahead of the curve and develop real-time personalized viewing experiences? What are the measures that can be taken to tame the abundant amounts of data and increase viewers' engagement and loyalty?

Daisy Intelligence announced that it has raised $10 million (CDN) in Series A financing led by Framework Venture Partners and partnered by European-based corporate investor, Sonae IM. The funding will enable Daisy to expand globally, invest in sales and marketing, provide further support for its customer success teams, and expand its operational infrastructure as growth demands. Daisy's AI-powered technology platform helps retailers and insurance companies generate significantly improved financial results by delivering business recommendations and automating complex processes beyond human capability.Daisy is driving a revolution in retail with its core AI SaaS platform, adding intelligence and automation to merchandising decisions. Daisy has helped its retail clients increase year over year, same store sales an average of 2.9% by optimizing their promotional product and pricing mix. Insurance companies use Daisy's AI-powered risk management platform to detect and avoid millions of dollars in fraud and automatically adjudicate claims.

In our environment, agents play a team-based hide-and-seek game. Hiders (blue) are tasked with avoiding line-of-sight from the seekers (red), and seekers are tasked with keeping vision of the hiders. There are objects scattered throughout the environment that hiders and seekers can grab and lock in place, as well as randomly generated immovable rooms and walls that agents must learn to navigate. Before the game begins, hiders are given a preparation phase where seekers are immobilized to give hiders a chance to run away or change their environment. There are no explicit incentives for agents to interact with objects in the environment; the only supervision given is through the hide-and-seek objective.

The financial sector has been among the fastest adaptors of AI algorithms, which are well suited to the industry's complex and fast-moving environment. At last week's Re•Work AI in Finance Conference in New York, researchers and engineers from banks and academia alike shared their thoughts on current AI research and applications in the finance world. IBM has built a blockchain-based infrastructure for federated AI, enabling institutions to leverage transaction data across branches to improve decision making. Alan King is an IBM AI and Blockchain Solutions engineer. In his presentation King spoke of the advantages of using federated AI on transaction data.

It's the year 2019--and the rise of the machines is transforming digital marketing. Nowadays, AI makes it much easier to generate information for online shoppers directly from a website or in the search results. Yes, that horror movie recommended in your streaming library is generated from AI. Artificial intelligence can both generate and curate content to personalize the user experience. The system is able to generate content based on matching data and information that has been indexed through the internet. For instance, you may notice this as a pop-up that appears as soon as a user visits your website.