The latest report published by Verified Market Research contains an in-depth analysis of the Artificial Intelligence Chipsets Market.

In this paper, we introduce a novel methodology to efficiently construct a corpus for question answering over structured data. For this, we introduce an intermediate representation that is based on the logical query plan in a database called Operation Trees (OT). This representation allows us to invert the annotation process without losing flexibility in the types of queries that we generate. Furthermore, it allows for fine-grained alignment of query tokens to OT operations. In our method, we randomly generate OTs from a context-free grammar. Afterwards, annotators have to write the appropriate natural language question that is represented by the OT. Finally, the annotators assign the tokens to the OT operations. We apply the method to create a new corpus OTTA (Operation Trees and Token Assignment), a large semantic parsing corpus for evaluating natural language interfaces to databases. We compare OTTA to Spider and LC-QuaD 2.0 and show that our methodology more than triples the annotation speed while maintaining the complexity of the queries. Finally, we train a state-of-the-art semantic parsing model on our data and show that our corpus is a challenging dataset and that the token alignment can be leveraged to increase the performance significantly.

With the success of deep learning, algorithms have pushed into another domain that humans thought was safe from automation: the creation of compelling art. AI-generated art has improved dramatically over the past several years, and the results can be seen in competitions like RobotArt and NVIDIA's DeepArt: But while these models are certainly an impressive technical accomplishment, a contentious point of discussion is whether AI and machine learning models are truly creative in the way humans are. Some have argued that it isn't really creative to build mathematical models of pixels in an image or to identify sequential dependencies in the structure of songs. AI, they claim, lacks the human touch. But it's also not clear that human brains are doing anything more impressive. How do we know that the artistic spark of a painter or musician isn't actually a mathematical model, trained -- like a neural network -- through constant practice?

Friedrichshafen, 15 April 2020 – CIMON-2, the updated version of the CIMON astronaut assistant, developed and built by Airbus for the German Aerospace Center Space Administration (DLR), has now demonstrated its capabilities during initial tests on the International Space Station (ISS). The free-flying, spherical technology demonstrator with artificial intelligence (AI) showed off a number of its features during interactions with ESA astronaut Luca Parmitano. CIMON-2 started its journey to the ISS on 05 December 2019, launching with the CRS-19 supply mission from the Kennedy Space Center in Cape Canaveral, Florida. It is scheduled to stay on the ISS for up to three years. Just shy of two months after the successful first use of CIMON-2, the project team has now received the analysis.

Interpretability of machine learning models has gained more and more attention among researchers in the artificial intelligence (AI) and human-computer interaction (HCI) communities. Most existing work focuses on decision making, whereas we consider knowledge discovery. In particular, we focus on evaluating AI-discovered knowledge/rules in the arts and humanities. From a specific scenario, we present an experimental procedure to collect and assess human-generated verbal interpretations of AI-generated music theory/rules rendered as sophisticated symbolic/numeric objects. Our goal is to reveal both the possibilities and the challenges in such a process of decoding expressive messages from AI sources. We treat this as a first step towards 1) better design of AI representations that are human interpretable and 2) a general methodology to evaluate interpretability of AI-discovered knowledge representations.

Carnegie Mellon University has shaped artificial intelligence (AI) from the field's very beginning. Today, researchers from all seven colleges across CMU continue to define AI as the next frontier in human progress and are working to help solve problems in areas from healthcare to education. Because of the interdisciplinary campus culture, CMU is a place where women are thriving in the field of AI, even though they are underrepresented in science, technology, engineering and math (STEM) across the globe. CMU's women in AI -- from undergraduate students to distinguished faculty members -- discuss their research, its impact and their future in the world of AI.

This week on Talking Pop Health, host Eric Tower welcomes Star Cunningham, CEO and founder of 4D Healthware, to the podcast for a conversation about the hot topic of virtual medicine, which has become a primary source of communicating with patients during the COVID-19 pandemic. Star discusses how 4D Healthware has utilized technology and health analytics to give patients the tools to take control of their health and lower their total cost of care. Star, who has a background in artificial intelligence, founded 4D Healthware in 2011. The company combines emerging technology, smart devices and health data analytics into a single platform that allows patients and providers to engage in RPM, Remote Patient Management. "Through our analytics, we can determine what time of day is best to contact patients. The patients are able to choose how they want to be communicated with, whether that be via a text message, email or phone call. Each patient is able to have a customized communication experience. The final piece to delivering them a customized experience is to ensure that the patient's needs are cared for when engaging with them."

I am a technology journalist with over two decades of press experience. Primarily I work as a news analysis writer dedicated to a software application development'beat'; but, in a fluid media world, I am also an analyst, technology evangelist and content consultant. As the previously narrow discipline of programming now extends across a wider transept of the enterprise IT landscape, my own editorial purview has also broadened. I have spent much of the last ten years also focusing on open source, data analytics and intelligence, cloud computing, mobile devices and data management. I have an extensive background in communications starting in print media, newspapers and also television.

We are living in a Great Epoche of Experiments. Think about Renaissance or Dadaist movements. But data science, empowered by the current state of the computational periphery, allows us to do something beyond our imagination. Machine learning was the initial start of the new journey into the unknown. Deep (and unsupervised) learning is a step further.