OpenAI's ChatGPT technology has become the talk of the town, with its capabilities seemingly drawn from the realms of science fiction. Impressive artworks and complex texts being produced without humans – so far so cool. But have you realised the potential privacy issues which the revolutionary technology potentially creates? This technology, along with rivals such as Google's Bard, is attracting millions of queries and searches every day. ChatGPT alone had gained over 100m users by January 2023, making it the fastest-growing consumer application ever.[1]

Vice President Kamala Harris on Wednesday explained artificial intelligence as she convened a roundtable with labor and civil rights leaders to talk about the technology. "AI Czar" and Vice President Kamala Harris was ridiculed on social media for her "stunning" description of artificial intelligence on Wednesday. In the latest example of Harris' "word salad" moments, the vice president spoke at the Eisenhower Executive Office Building in Washington, D.C., and gave what people saw as a condescending and long-winded description of AI. "I think the first part of this issue that should be articulated is AI is kind of a fancy thing," Harris said. It means artificial intelligence, but ultimately what it is, is it's about machine learning." She added, "And so, the machine is taught -- and part of the issue here is what information is going into the machine that will then determine -- and we can predict then, if we think about what information is going in, what then will be produced in terms of decisions and opinions that may be made through that process." Vice President Kamala Harris speaks during a meeting with civil rights leaders and consumer protection experts to discuss the societal impact of artificial intelligence, in the Eisenhower Executive Office building in Washington, DC, on July 12, 2023. "So to reduce it down to its most simple point, this is part of the issue that we have here is thinking about what is going into a decision, and then whether that decision is actually legitimate and reflective of the needs and the life experiences of all the people," Kamala concluded. "Kamala Harris talks to Americans like we are all in kindergarten.

The agency's focus on such fabrications comes after numerous high-profile reports of the chatbot producing incorrect information that could damage people's reputations. Mark Walters, a radio talk show host in Georgia sued OpenAI for defamation, alleging the chabot made up legal claims against him. The lawsuit alleges that ChatGPT falsely claimed that Walters, the host of "Armed American Radio," was accused of defrauding and embezzling funds from the Second Amendment Foundation. The response was provided in response to a question about a lawsuit about the foundation that Walters is not a party to, according to the complaint.

Google's parent company has announced the rollout of its chatbot rival to ChatGPT in the European Union and Brazil, as tech firms ramp up their competition to dominate artificial intelligence. Bard is now available in 27 EU countries and Brazil, as well as 40 new languages, including Arabic, Chinese, German, Hindi and Spanish, Alphabet said on Thursday. "Curiosity and imagination are the driving forces behind human creativity," Bard's product lead Jack Krawczyk and vice president Amarnag Subramanya said in a blog post. "Whether it's a child inventing a game, friends dreaming up their next adventure, or an entrepreneur coming up with a new business idea, our ability to imagine new possibilities is one of our most innate human qualities. That's why we created Bard: to help you explore that curiosity, augment your imagination and ultimately get your ideas off the ground – not just by answering your questions, but by helping you build on them."

Fox News senior national security correspondent Jennifer Griffin has the latest on the military shortfall on'The Story.' If you turn on your television and radio, or log onto any social media platform you'll likely hear talking heads offering opinions on our country's inflation and economic recession problems. This is a very grave threat to the well-being of Americans, induced by a bloated government and excessive government spending. However, one thing noticeably absent from the national discussions is perhaps the most dire kind of recession – a recession of recruitment for our military. Long-term, the failure of our Armed Forces to meet their recruiting objectives across all branches could pose far worse consequences for the United States than the sluggish economy.

Heritage Foundation tech policy research associate Jake Denton joined'Fox & Friends First' to discuss growing concerns surrounding the political implications of artificial intelligence. AI's breakthrough into popular culture, marked by chatbot tools like ChatGPT, has turned this technology into a battleground for culture warriors. However, equating artificial intelligence or AI with social media platforms could cost us significant advances in healthcare, transportation and global leadership in technology. Over the past decade, politicians have developed a playbook for scoring political points by criticizing social media. Democrats have focused on the spread of misinformation and disinformation, while Republicans have raised concerns about perceived bias against conservative views.

Winnow CEO Marc Zornes and Iberostar Group's Dr. Megan Morikawa discuss how artificial intelligence can target food waste in commercial kitchens -- and improve both business efficiency and global sustainability. Food waste makes up an estimated 30% to 40% of the food supply, according to the U.S. Department of Agriculture -- and now a London company is using artificial intelligence in an attempt to address the problem. Winnow, a food waste solution company, has developed an AI-powered system that aims to reduce food waste in commercial kitchens worldwide. CEO Marc Zornes said the company's tech can measure the foods that get tossed daily using machine learning and a camera. "We use computer vision to identify what's being wasted in real time, literally as the food's being thrown away," he told Fox News Digital in an interview.

In their seminal 1990 paper, Wasserman and Kadane establish an upper bound for the Bayes' posterior probability of a measurable set $A$, when the prior lies in a class of probability measures $\mathcal{P}$ and the likelihood is precise. They also give a sufficient condition for such upper bound to hold with equality. In this paper, we introduce a generalization of their result by additionally addressing uncertainty related to the likelihood. We give an upper bound for the posterior probability when both the prior and the likelihood belong to a set of probabilities. Furthermore, we give a sufficient condition for this upper bound to become an equality. This result is interesting on its own, and has the potential of being applied to various fields of engineering (e.g. model predictive control), machine learning, and artificial intelligence.

Surveys are an important research tool, providing unique measurements on subjective experiences such as sentiment and opinions that cannot be measured by other means. However, because survey data is collected from a self-selected group of participants, directly inferring insights from it to a population of interest, or training ML models on such data, can lead to erroneous estimates or under-performing models. In this paper we present balance, an open-source Python package by Meta, offering a simple workflow for analyzing and adjusting biased data samples with respect to a population of interest. The balance workflow includes three steps: understanding the initial bias in the data relative to a target we would like to infer, adjusting the data to correct for the bias by producing weights for each unit in the sample based on propensity scores, and evaluating the final biases and the variance inflation after applying the fitted weights. The package provides a simple API that can be used by researchers and data scientists from a wide range of fields on a variety of data. The paper provides the relevant context, methodological background, and presents the package's API.

Exfiltration of data via email is a serious cybersecurity threat for many organizations. Detecting data exfiltration (anomaly) patterns typically requires labeling, most often done by a human annotator, to reduce the high number of false alarms. Active Learning (AL) is a promising approach for labeling data efficiently, but it needs to choose an efficient order in which cases are to be labeled, and there are uncertainties as to what scoring procedure should be used to prioritize cases for labeling, especially when detecting rare cases of interest is crucial. We propose an adaptive AL sampling strategy that leverages the underlying prior data distribution, as well as model uncertainty, to produce batches of cases to be labeled that contain instances of rare anomalies. We show that (1) the classifier benefits from a batch of representative and informative instances of both normal and anomalous examples, (2) unsupervised anomaly detection plays a useful role in building the classifier in the early stages of training when relatively little labeling has been done thus far. Our approach to AL for anomaly detection outperformed existing AL approaches on three highly unbalanced UCI benchmarks and on one real-world redacted email data set.