Presidents Clinton, George H.W. Bush, Obama, Biden and Trump all pay respect to Jimmy Carter at his state funeral in Washington, D.C.. During the 2024 campaign cycle, Americans witnessed what appeared to be no love lost between President-elect Donald Trump and former President Barack Obama. However, at former President Jimmy Carter's funeral the two recent presidents appeared to be enjoying each other's company and largely ignored other dignitaries arriving around them, including Vice President Kamala Harris and President Biden. Susan Constantine, a communication and body language expert, said Harris came off "as cool as could be." When she was walking she was very robotic.

Fox News host Greg Gutfeld gives his take on Democrats' fears about former President Trump winning the 2024 election on'Gutfeld!' Former President Obama is becoming increasingly anxious about the closeness of the 2024 presidential election and fears former President Trump could take back the White House, according to a report. Former Attorney General Eric Holder, one of Obama's closest confidants, told USA Today that if Trump were to win the Republican nomination and beat President Biden this November, there could be "incalculable damage" brought upon the country. Holder confirmed Obama "absolutely" holds the same views when asked by the publication. "I think that's what motivates him. I think that's what will continue to motivate him," Holder responded.

A common practice in Natural Language Processing (NLP) is to visualize the text corpus without reading through the entire literature, still grasping the central idea and key points described. For a long time, researchers focused on extracting topics from the text and visualizing them based on their relative significance in the corpus. However, recently, researchers started coming up with more complex systems that not only expose the topics of the corpus but also word closely related to the topic to give users a holistic view. These detailed visualizations spawned research on comparing text corpora based on their visualization. Topics are often compared to idealize the difference between corpora. However, to capture greater semantics from different corpora, researchers have started to compare texts based on the sentiment of the topics related to the text. Comparing the words carrying the most weightage, we can get an idea about the important topics for corpus. There are multiple existing texts comparing methods present that compare topics rather than sentiments but we feel that focusing on sentiment-carrying words would better compare the two corpora. Since only sentiments can explain the real feeling of the text and not just the topic, topics without sentiments are just nouns. We aim to differentiate the corpus with a focus on sentiment, as opposed to comparing all the words appearing in the two corpora. The rationale behind this is, that the two corpora do not many have identical words for side-by-side comparison, so comparing the sentiment words gives us an idea of how the corpora are appealing to the emotions of the reader. We can argue that the entropy or the unexpectedness and divergence of topics should also be of importance and help us to identify key pivot points and the importance of certain topics in the corpus alongside relative sentiment.

During President Obama's two terms in the White House, Jason Furman was a top economic policy adviser and a key voice on the growing importance of artificial intelligence. Furman served as deputy director of the National Economic Council before becoming chair of the Council of Economic Advisers. He also coauthored a report issued by the Obama administration in October 2016 that detailed the economic importance of AI to the US. Furman, who is now a professor of the practice of economic policy at Harvard, spoke to WIRED senior writer Will Knight. This transcript has been edited for length and clarity.

It is well known that deep generative models have a rich latent space, and that it is possible to smoothly manipulate their outputs by traversing this latent space. Recently, architectures have emerged that allow for more complex manipulations, such as making an image look as though it were from a different class, or painted in a certain style. These methods typically require large amounts of training in order to learn a single class of manipulations. We present Transflow Learning, a method for transforming a pre-trained generative model so that its outputs more closely resemble data that we provide afterwards. In contrast to previous methods, Transflow Learning does not require any training at all, and instead warps the probability distribution from which we sample latent vectors using Bayesian inference. Transflow Learning can be used to solve a wide variety of tasks, such as neural style transfer and few-shot classification.

For tomorrow's workers, AI will be more than a tool; AIs will be their co-workers and a ubiquitous part of their lives. If the next generation is to use AI and big data effectively – if they're to understand their inherent limitations, and build even better platforms and intelligent systems -- we need to prepare them now. That will mean some adjustments in elementary education and some major, long-overdue upgrades in computer science instruction at the secondary level. The U.S. is woefully behind many of our peer nations, and President Obama's Computer Science for All initiative may flounder amid budget cuts proposed by the Trump administration. Another major hurdle is that our schools face a severe shortage of teachers who are trained in computer science.

Actually, most people have very little knowledge of how artificial intelligence works, or for that matter, how broadly it is used in everything from daily financial transactions to determining your credit score. Take the stock market, for example. Only a tiny amount of trading on Wall Street is carried out by human beings. The overwhelming majority of trading is algorithmic in nature. It's preprogrammed so that if the price of soybeans or oil goes down, all kinds of additional steps will take place.

WASHINGTON: "I think human extinction will probably occur," said billionaire entrepreneur Elon Musk, "and technology will likely play a part in this." He does not include his Tesla motor company or SpaceX endeavors among the malevolent innovations threatening humanity. Instead, Musk insists our demise is the exclusive responsibility of Artificial Intelligence (AI). The algorithms that teach computers to interact with us have produced software that seems almost human. They mark our calendars, compile recipes, tailor music and advertising to our tastes, and switch on the house lights at dusk.

I had the opportunity to glimpse a large part of the future this week. I think you will find it as amazing as I did. Dr. Kiron Skinner, a remarkable scholar, hosted me at Carnegie Mellon University in Pittsburgh. According to the U.S. News & World Report's "Best Colleges" rankings, Carnegie Mellon has the nation's top graduate level artificial intelligence (AI) program and one of the top five undergraduate computer engineering programs. On Wednesday, the people at Carnegie Mellon showed me a few of the reasons they are held in such high regard.

New concerns over how artificial intelligence videos could spread fake news and even prompt a war. Forget fake news for a moment. Artificial intelligence is now able to generate a convincing video of a celebrity or public figure. For illicit purposes, these videos are called deepfakes and show a celebrity superimposed into an adult movie. A programmer finds existing video and audio for a known figure, then the AI takes over and creates a brand new version.