Despite the availability of qualified research personnel, up-to-date research facilities and experience in developing applied research and innovation, many worldwide research institutions face difficulties when managing contracted Research and Development (R&D) projects due to expectations from Industry (private sector). Such difficulties have motivated funding agents to create evaluation processes to check whether the operational procedures of funded research institutions are sufficient to provide timely answers to demand for innovation from industry and also to identify aspects that require quality improvement in research development. For this purpose, several multiple criteria decision-making approaches can be applied. Among the available multiple criteria approaches, sorting methods are one prominent tool to evaluate the operational capacity. However, the first difficulty in applying multiple criteria sorting methods is the need to hierarchically structure multiple criteria in order to represent the intended decision process. Additional challenges include the elicitation of the preference information and the definition of criteria evaluation, since these are frequently affected by some imprecision. In this paper, a new sorting method is proposed to deal with all of those critical points simultaneously. To consider multiple levels for the decision criteria, the FlowSort method is extended to account for hierarchical criteria. To deal with imprecise data, the FlowSort is integrated with fuzzy approaches. To yield solutions that consider fluctuations from imprecise weights, the Stochastic Multicriteria Acceptability Analysis is used. Finally, the proposed method is applied to the evaluation of research institutions, classifying them according to their operational maturity for development of applied research.

Artificial intelligence has had a profound impact on finance. In the span of a few decades, it has made finance faster, more accessible, more profitable, and more efficient in many ways. Despite all the significant benefits made possible by financial artificial intelligence, it also presents serious risks and implications for law, business, and society. My recent article, 'Artificial Intelligence, Finance, and the Law', published in the Fordham Law Review, offers a study of those risks and implications. It provides a broad examination of the inherent risks and larger implications of financial artificial intelligence.

Artificial intelligence is coming for America's high-paid professions as it creates winners and losers across the labor market like never before. White-collar jobs and better-educated occupations along with production workers are among the most susceptible to AI's spread into the economy, according to a Brookings Institution report Wednesday that draws on a new analysis of patent data by a Stanford University economist. "Just as the impacts of robotics and software tend to be sizable and negative on exposed middle- and low-skill occupations, so AI's inroads are projected to negatively impact higher-skill occupations," researchers Mark Muro, Jacob Whiton and Robert Maxim wrote. Workers with graduate or professional degrees will be almost four times as exposed to AI as workers with just a high school degree, the report showed. The researchers also concluded that AI appears most likely to affect men, prime-age and white and Asian American workers.

In the first week of November, Apple and Goldman Sachs got a bit of unwanted attention when @DHH, the famous creator of Ruby on Rails (and a Le Mans 24h race class winning driver) accused them of gender discrimination. Case in point: He and his wife applied for Apple Cards together and received a credit limit 20 times of was given for his wife. This, when they file joint taxes and she has a better credit score. The Tweet went viral, but things got even more heated when the other "Steve" of Apple, @stevewoz backed the claim. But we are not discussing the troubles of Apple and Goldman Sachs after this incident, and the subsequent legal inquiry that was ordered.

A specialist technology app store and artificial intelligence for contract drafting are among the technologies likely to be adopted by the legal arm of Big Four firm Deloitte in an attempt to bridge the gap between startup companies and clients. Deloitte Legal said today that it had'hand-selected' 14 startups after evaluating 400 businesses for its Deloitte Legal Ventures programme. In a significant departure from previous lawtech incubator schemes, Deloitte Legal has said it will become a user of products and services offered by the chosen startups. These include execution technology, artificial intelligence, data analytics and predictive analytics, the firm said. Among the 12 companies named today are Genie AI, a UK pioneer in artificial intelligence and Reynen Court, an online platform designed to simplify the process of buying legal technology.

Qiu Rui, a policeman in Chongqing, was on duty this summer when he received an alert from a facial recognition system at a local square. There was a high probability a man caught on camera was a suspect in a 2002 murder case, the system told him. The depth, breadth and intrusiveness of China's mass surveillance may be unprecedented in modern history The city's surveillance system scans facial features of people on the streets from frames of video footage in real time, creating a virtual map of the face. It can then match this information against scanned faces of suspects in a police database. If there is a match that passes a preset threshold, typically 60% or higher, the system immediately notifies officers.

Qiu Rui, a policeman in Chongqing, was on duty this summer when he received an alert from a facial recognition system at a local square. There was a high probability a man caught on camera was a suspect in a 2002 murder case, the system told him. The depth, breadth and intrusiveness of China's mass surveillance may be unprecedented in modern history The city's surveillance system scans facial features of people on the streets from frames of video footage in real time, creating a virtual map of the face. It can then match this information against scanned faces of suspects in a police database. If there is a match that passes a preset threshold, typically 60% or higher, the system immediately notifies officers.

There are a lot of different legal issues businesses need to deal with when considering implementing artificial intelligence (AI) including intellectual property rights, privacy and civil liability concerns. Artificial intelligence is the use of technology to replace human thought, explained Adam Allouba, partner at Dentons. AI runs on data which is used to train algorithms and intellectual property is the idea of the ownership in a database or a compilation of data. "You've really got to think about'do I have the rights for the data I'm using?'… It's very important to think those things through because if you don't your whole system might be trained on something that is not compliant with law which could open the door to lawsuits."

When we give a machine values and it solves a calculation for us, that's simply computing. When we give a machine data and it learns from its experiences and then makes recommendations, that's artificial intelligence. So what happens when we give AI one of the most human of art forms: music? Quite a bit, as it turns out. AI uses machine learning models to produce new patterns and correlations based on the data it was trained from.

If recent television shows are anything to go by, we're a little concerned about the consequences of technological development. Black Mirror projects the negative consequences of social media, while artificial intelligence turns rogue in The 100 and Better Than Us. The potential extinction of the human race is up for grabs in Travellers, and Altered Carbon frets over the separation of human consciousness from the body. And Humans and Westworld see trouble ahead for human-android relations. Narratives like these have a long lineage.