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.

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.

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.

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.

The News: Oracle is pushing the envelope on what NVIDIA GPUs can do in the cloud. Find out how, next week at Oracle OpenWorld and Code One in San Francisco, where NVIDIA and Oracle will showcase their growing collaboration to bring AI and GPU-accelerated applications to the enterprise. Integrating CUDA-X libraries into GraalVM applications, enhancing conversational AI with Oracle Digital Assistant, and accelerating data science pipelines through the Oracle Cloud Infrastructure Data Science service are a few examples of how enterprise customers and developers worldwide will benefit from GPU-accelerated computing. The companies first teamed up by bringing bare metal GPUs to the public cloud through Oracle Cloud Infrastructure, fueling innovation across a broad range of industries. Engineers, developers, data scientists and researchers are using these instances to power visualization, AI/machine learning, big data, database and HPC workloads.

National Workshops to Explore Long-range Strategies 7. Collect suggestions from the national planning workshops, distilled in to 93 actions, assess all via five (5) Real-Time Delphi's 8. Final Report for Public Discussion 10.

Seeking to fight future wars with indigenous artificial intelligence (AI), a top Army officer on Friday said, India will soon catch up with the world in terms of advancements in the modern warfare technology and AI is going to be the next most disruptive technology in military affairs. Indicating departure from the conventional war techniques, South Western Army Commander Lieutenant General Alok Kler said, "It's time to incorporate assisted decision making in future warfare to be more efficient and more accurate." "AI is the next most disruptive technology in the revolution of Military Affairs as it is going to make the equipment more lethal," he added. Speaking on the possibility of a complete AI war, the official said that we can use AI in the next 10 years for decision making but a war based on AI is a distant possibility. Highlighting the technological race, the official said that India is a late starter in the field of artificial intelligence but will slowly catch up really quickly and added that most of the infotech brains are coming from South Asia.

Introducing--artificial intelligence--(AI) to sales training and coaching can provide a more individualized learning experience that can scale across the organization, according to Gartner, Inc. Creating a high-performing sales organization is difficult with traditional training and coaching technology as coaching content and recommendations are generally delivered by role to the sales organization and do not account for individuals-- learning styles. The use of complex--machine learning--algorithms and AI can guide reps and sales managers with recommendations for training and coaching based on their learning style. These technologies utilize branching, a method to guide an individual--s learning through a module based on responses, as well as adaptive learning, where the system directs the learner to appropriate training or coaching based on their interaction with the system. In a--Gartner survey--of organizations that are piloting or deploying AI technologies, 61% of respondents reported the resulting value delivered to the organization as significant. When asked how AI will improve their sales organization, respondents cited increased efficiency, cost reduction and improved revenue streams.

Artificial intelligence (AI) will have a profound, positive and transformative impact on the health of European and worldwide populations. There is huge interest and worldwide investment in general AI, and health systems are one of the most important application areas. Europe is uniquely placed to play a leadership role in the race to build better, more accurate and safer health technology systems through the use of AI. European researchers are at the forefront in the development of safe, robust and effective AI delivering substantive patient and public benefit. If properly managed, AI research will deliver both sustained health improve ments as well as economic benefits to Europe.

As it is across the world, the amount of investments made in artificial intelligence (AI) and the number of entrepreneurs using AI to develop new projects is also increasing in Turkey. AI facilitates business and production processes and supports automation. This shows that the number of AI start-ups will increase in the coming years. Therefore, Lale Deliveli Alp, one of the founders of Deliveli Alp Law & Consultancy, answered the following questions to make young entrepreneurs' work easier: By what legal means are AI software developed by technology companies protected? Is it possible to patent a developed AI? Alp answered the question about which many entrepreneurs wonder with the following response: "It is not possible to patent AI, which is software, in accordance with Article No. 82 in the Industrial Property Law. Computer programs are out of patentability. However, if the developed AI does not function separately from the hardware, it can be patented. For example, the AI of a developed robot can be patented since it cannot be used separately from the robot. What about the designs developed by AI, poems and paintings by it? A work must be new and exceed the known limits of the technique in order to be protected by patent law, namely to be patentable. The solution of whether an invention developed by totally AI is protected by patent law is hidden in the answer of whether an AI is accepted as inventive by law."