In February 2020, the digital media agency Space150 experimented with artificial intelligence machine learning programs to create the excellently …

The Palo Alto-based startup launched a car insurance comparison service using artificial intelligence and machine learning in January 2019.

Artificial intelligence (AI) is being used to evaluate tissue samples in men who produce low levels of, or no, sperm.

My master degree's thesis consists in a tool that detects and classifies Magic: the Gathering cards during a tabletop streaming (no online games). MtG official tournaments have some streaming procedures to follow, like a top-view camera, card positioning on the table, a certain lighting etc that can help the machine a lot by avoiding harsh conditions. Main Issue: There are around 20.000 different cards, and some of them have more than 1 image representing em (alternative artworks or frames). Is classification with that number of classes even doable in a reasonable training time? How to react: I could accept training on a smaller subset (like maybe the newest cards), but I would like to create something useful, and not just a demonstrative project.

Research on social stratification is closely linked to analysing the prestige associated with different occupations. This research focuses on the positions of occupations in the semantic space represented by large amounts of textual data. The results are compared to standard results in social stratification to see whether the classical results are reproduced and if additional insights can be gained into the social positions of occupations. The paper gives an affirmative answer to both questions. The results show fundamental similarity of the occupational structure obtained from text analysis to the structure described by prestige and social distance scales. While our research reinforces many theories and empirical findings of the traditional body of literature on social stratification and, in particular, occupational hierarchy, it pointed to the importance of a factor not discussed in the main line of stratification literature so far: the power and organizational aspect.

Low-complexity models such as linear function representation play a pivotal role in enabling sample-efficient reinforcement learning (RL). The current paper pertains to a scenario with value-based linear representation, which postulates the linear realizability of the optimal Q-function (also called the "linear $Q^{\star}$ problem"). While linear realizability alone does not allow for sample-efficient solutions in general, the presence of a large sub-optimality gap is a potential game changer, depending on the sampling mechanism in use. Informally, sample efficiency is achievable with a large sub-optimality gap when a generative model is available but is unfortunately infeasible when we turn to standard online RL settings. In this paper, we make progress towards understanding this linear $Q^{\star}$ problem by investigating a new sampling protocol, which draws samples in an online/exploratory fashion but allows one to backtrack and revisit previous states in a controlled and infrequent manner. This protocol is more flexible than the standard online RL setting, while being practically relevant and far more restrictive than the generative model. We develop an algorithm tailored to this setting, achieving a sample complexity that scales polynomially with the feature dimension, the horizon, and the inverse sub-optimality gap, but not the size of the state/action space. Our findings underscore the fundamental interplay between sampling protocols and low-complexity structural representation in RL.

AIOps platforms leverage big data, machine learning, and analytics to enhance IT operations via monitoring, automation, and service desk functions …

Android Machine Learning also has a few sessions.

A deep-down analysis reveals that the company indeed has some key … the company's highly efficient machine learning process in the medium term.

In the movie All The President's Men, Woodward and Bernstein meet their informant in a parking garage who tells them: "Follow the money." If you want to know which technologies are hot in banking, you should do the same. The truly "hot" technologies in banking are the ones that financial institutions invest in--not necessarily the ones the pundits talk about. At the end of the past seven years, Cornerstone Advisors has surveyed financial institutions to find out where their technology dollars will go in the coming year. In Cornerstone's What's Going On in Banking 2021 study, the top five technologies for 2021 are: 1) Digital account opening; 2) Application programming interfaces (APIs); 3) Video collaboration; 4) P2P payments; and 5) Cloud computing.