His demand for a ban triggered a legal and moral quagmire, as the Pentagon faced the prospect of throwing out service members who had willingly come forward as transgender after being promised they would be protected and allowed to serve. And as legal battles blocked the ban from taking effect, the Obama-era policy continued and transgender individuals were allowed to begin enlisting in the military a little more than a year ago.

WASHINGTON – The Defense Department has approved a new policy that will largely bar most transgender troops and military recruits from transitioning to another sex, and require most individuals to serve in their birth gender. The new policy comes after a lengthy and complicated legal battle, and it falls short of the all-out transgender ban that was initially ordered by President Donald Trump. But it will likely force the military to eventually discharge transgender individuals who need hormone treatments or surgery and can't or won't serve in their birth gender. The order says the military services must implement the new policy in 30 days, giving some individuals a short window of time to qualify for gender transition if needed. And it allows service secretaries to waive the policy on a case-by-case basis.

For further information on the Summer School on AI & Law, you can write to: aiandlaw@eui.eu.

In addition to being fascinating, Artificial Intelligence (AI), is a game changer. It will have a great impact that go far beyond corporate profits or the economy. It will open vast new opportunities, but also reopen old technical, economical and even philosophical debates. And how should we evaluate it? There is a lot of confusion about what AI is. The term is now so overused it is approaching the status of marketing jargon. Scanning market research reports, it is clear that too many software companies now describe their products as "AI".

Machine learning technology will provide the best results in detection of fraud in the future. Indeed, many organizations are actively driving replacement of human-driven rules analysis with machine-driven solutions. However, I believe that a mix of machined-led and human-led activity is the best fit for many organizations to maximize performance. There are several perceptions that suggest fraud rules are no longer fit. Ultimately a data-driven approach, regardless of human or machine involvement, is a state that organizations need to move to in order to maximize detection in the present and to ease the transition to a more machine-driven future.

Although the headline that gets the clicks is "AI is taking our jobs," the current reality is that "Automation is replacing some of our tasks." I know, it's nowhere near as catchy. I say automation rather than AI because it doesn't really matter what technology the underlying system uses, so long as it does the job well. For example, being a bank teller was a human job requiring intelligence, but there's no AI in an ATM cash machine (when it comes to technology evolution, some of them don't even seem to have caught up to Windows Vista yet). I also prefer "tasks" over "jobs" because mostly AI can only do certain elements.

Multilateral solutions remain the best way to address threats to international peace and security. This is true when we talk about the situation in Syria, Libya, Yemen or the Middle East peace process.

Interpretability has always been a major concern for fuzzy rule-based classifiers. The usage of human-readable models allows them to explain the reasoning behind their predictions and decisions. However, when it comes to Big Data classification problems, fuzzy rule-based classifiers have not been able to maintain the good trade-off between accuracy and interpretability that has characterized these techniques in non-Big Data environments. The most accurate methods build too complex models composed of a large number of rules and fuzzy sets, while those approaches focusing on interpretability do not provide state-of-the-art discrimination capabilities. In this paper, we propose a new distributed learning algorithm named CFM-BD to construct accurate and compact fuzzy rule-based classification systems for Big Data. This method has been specifically designed from scratch for Big Data problems and does not adapt or extend any existing algorithm. The proposed learning process consists of three stages: 1) pre-processing based on the probability integral transform theorem; 2) rule induction inspired by CHI-BD and Apriori algorithms; 3) rule selection by means of a global evolutionary optimization. We conducted a complete empirical study to test the performance of our approach in terms of accuracy, complexity, and runtime. The results obtained were compared and contrasted with four state-of-the-art fuzzy classifiers for Big Data (FBDT, FMDT, Chi-Spark-RS, and CHI-BD). According to this study, CFM-BD is able to provide competitive discrimination capabilities using significantly simpler models composed of a few rules of less than 3 antecedents, employing 5 linguistic labels for all variables.

Finding hard instances, which need a long time to solve, of graph problems such as the graph coloring problem and the maximum clique problem, is important for (1) building a good benchmark for evaluating the performance of algorithms, and (2) analyzing the algorithms to accelerate them. The existing methods for generating hard instances rely on parameters or rules that are found by domain experts; however, they are specific to the problem. Hence, it is difficult to generate hard instances for general cases. To address this issue, in this paper, we formulate finding hard instances of graph problems as two equivalent optimization problems. Then, we propose a method to automatically find hard instances by solving the optimization problems. The advantage of the proposed algorithm over the existing rule based approach is that it does not require any task specific knowledge. To the best of our knowledge, this is the first non-trivial method in the literature to automatically find hard instances. Through experiments on various problems, we demonstrate that our proposed method can generate instances that are a few to several orders of magnitude harder than the random based approach in many settings. In particular, our method outperforms rule-based algorithms in the 3-coloring problem.

The Industrial Revolution conjures up images of steam engines, textile mills, and iron workers. This was a defining period during the late 18th and early 19th centuries, as society shifted from primarily agrarian to factory-based work. A second phase of rapid industrialization occurred just before World War I, driven by growth in steel and oil production, and the emergence of electricity. Fast-forward to the 1980s, when digital electronics started having a deep impact on society--the dawning Digital Revolution. Building on that era is what's called the Fourth Industrial Revolution.