They are a fundamental datatype in all the modern programming languages, and operations on strings frequently occur in disparate fields such as software analysis, model checking, database applications, web security, bioinformatics and so on[3, 11, 19, 21, 27, 28, 49, 60, 67]. Reasoning over strings requires solving arbitrarily complex string constraints, i.e., relations defined on a number of string variables. Typical examples of string constraints are string length, (dis-)equality, concatenation, substring, regular expression matching. With the term "string constraint solving" (in short, string solving or SCS) we refer to the process of modelling, processing, and solving combinatorial problems involving string constraints. We may see SCS as a declarative paradigm which falls at the intersection between constraint solving and combinatorics on words: the user states a problem with string variables and constraints, and a suitable string solver seeks a solution for that problem. Although works on the combinatorics of words were already published in the 1940s [110], the dawn of SCS dates back to the late 1980s in correspondence with the rise of Constraint Programming (CP) [114] and Constraint Logic Programming(CLP)[73] paradigms. Pioneers in this field were for example Trilogy[142], a language providing strings, integer and real constraints, and CLP(Σ) [144], an instance of the CLP scheme representing strings as regular sets. The latter in particular was the first known attempt to use string constraints like regular membership to denote regular sets.

"What would a candle inspired by your book smell like?" This was a question that was going around the other day, and most authors got to answer with something nice. My book, however… let's just say it features a neural net-generated recipe called "Basic Clam Frosting". But what kind of candles would a neural net invent? Training a neural net to generate candles was a good (and rather frightening) suggestion.

In the global rush to develop artificial intelligence (AI), the US, European Union and China have taken an early lead in published research papers – but they are often drawing on talent from India, Turkey and other countries, according to a new database launched by the Organisation for Economic Cooperation and Development. The OECD database – part of a growing inter-governmental effort to track AI trends and policies – indicates India has produced three times as many people with AI-related skills as the average for the 20 biggest economies in the world. And, the database shows, Indian AI experts are also the most likely in the world to have used their skills to get work abroad – often in the US or Germany, two of the biggest importers of AI talent. Besides India, other major exporters of global AI talent include Turkey and China. These complex patterns of AI talent moving around the world come as concerns grow in several capitals that governments need to step up local training in AI, if they want to stay competitive globally.

The European Union unveiled proposals Wednesday to regulate artificial intelligence that call for strict rules and safeguards on risky applications of the rapidly developing technology. The report is part of the bloc's wider digital strategy aimed at maintaining its position as the global pacesetter on technological standards. Big tech companies seeking to tap Europe's vast and lucrative market, including those from the U.S. and China, would have to play by any new rules that come into force. The EU's executive Commission said it wants to develop a "framework for trustworthy artificial intelligence." European Commission President Ursula von der Leyen had ordered her top deputies to come up with a coordinated European approach to artificial intelligence and data strategy 100 days after she took office in December. "We will be particularly careful where essential human rights and interests are at stake," von der Leyen told reporters in Brussels.

The medical and healthcare categories have been leading robotic innovation for decades. Look no further than Intuitive Surgical, whose da Vinci robot has been performing surgery since it received FDA clearance in the early 2000s. These days, the SRI spinoff is currently valued at more than $60 billion. There's a lot of money to be made for established companies and still areas to be explored for young startups, both on and off the operating table. The venture community has been betting big on companies developing everything from new surgical robots, assistive robots for medical facilities, robotic medical aid devices or otherwise.

In the fight against coronavirus, doctors have been given a helping hand, thanks to coronavirus patrol robots . The robots are being used in hospitals in Shenyang in China's northeastern Liaoning province, in the hopes of preventing the virus from spreading. The bots can quickly check people's temperatures and identities, and even disinfect them, according to AFP. AFP explained: "The hospital uses the robot to reduce the pressure on front-line medical staff and to avoid cross infections from the COVID-19 coronavirus." These aren't the only robots being used to curb the spread of the coronavirus.

When no new leads emerge in a murder or missing persons investigation, police must shift their resources to cases that offer new information. Currently, the FBI Uniform Crime Report keeps an estimated 250,000 cold cases on file, on which it co-operates with local law enforcement. Recent developments in AI, however, have shed light on some of these old and cold cases. For example, in 2018, DNA evidence submitted through a popular genealogy database enabled charges to be laid in more than a dozen cases involving the Golden State Killer who had been living " under the radar" for decades. According to a recent article in Forensic Science International, in 2018, direct-to-consumer (DTC) genetic genealogy databases were used to solve over fifty cold cases of either murder or missing persons.

Fremont, CA: The digital economy today is powered by big data. Generated in abundance by both individuals and enterprises, these data is stored in large data centers and some of which cover hundreds of thousands of square feet. Technology vendors are implementing pre-enriched machine-readable data, specific to given industries to speed time-to-market for custom-built AI tools. These kits are intended to help data scientists and AI engineers and include the data necessary to speed up the creation of AI models. Big data vendors had to take up the issue of data governance, security, and management, taking a back seat to accessibility and speed.

In the U.S. and other countries, aging populations and growing logistics demand have resulted in shortages of truck drivers. Autonomous trucks could help relieve those shortages. Einride AB today announced that it plans to hire what it called "the first autonomous and remote truck operator in the freight mobility space." The Stockholm-based company said it will hire drivers in Sweden next month, followed by the U.S. in the third quarter. The remote operators would begin commercial services in Sweden in Q3 2020 and in the U.S. in Q4 2020.

The newest roommate app, Badi, has made its official debut into the New York market. The artificial intelligence app took Europe by storm in September 2015. England, Germany, and Spain to be exact. The company has changed the lives of 2 million users by providing about 300,000 room listings. Badi uses an algorithm based on information such as age, gender, interests, and lifestyle preferences to assist users in finding rooms on the platform.