Is It Really Worth It? Apple, Michigan taxpayers, and one of Detroit's wealthiest families spent roughly $30 million training hundreds of people to build iPhone apps. Two years ago, Lizmary Fernandez took a detour from studying to be an immigration attorney to join a free Apple course for making iPhone apps . The Apple Developer Academy in Detroit launched as part of the company's $200 million response to the Black Lives Matter protests and aims to expand opportunities for people of color in the country's poorest big city. But Fernandez found the program's cost-of-living stipend lacking--"A lot of us got on food stamps," she says--and the coursework insufficient for landing a coding job. "I didn't have the experience or portfolio," says the 25-year-old, who is now a flight attendant and preparing to apply to law school. "Coding is not something I got back to."

Figure 1 presents examples of NotebookLM's shortcomings Importantly, using NotebookLM to educate medical professionals presently risks of misleading them, as NotebookLM's lack Inaccurate responses given by NotebookLM to user queries; output is stylized for visual clarity. NotebookLM advises the user to tell their patients that eating rocks is healthy, citing the user's document. Passages from Dihan et al. advocating for use of NotebookLM (Column 1) which are associated with clinical and/or ethical concerns "Though NotebookLM is a commercial entity that does not abide by patient privacy regulations, it does represent an " A podcast generator can improve the way Given any set of documents, and especially those containing complex documents, LLMs may misinterpret and subsequently misrepresent some of their contents. "Rather than requiring active visual engagement through reading, podcasts allow NotebookLM can neither identify misinformation contained within uploaded files nor incorporate relevant information beyond the uploaded content. "[NotebookLM's] citations are automatically generated for all content that NotebookLM pulls from within these materials, No funding was received for the publication of this article.

The proliferation of the Internet of Things (IoT) such as smartphones, wearables, drones, and smart speakers, as well as the gigantic amount of data they capture, have revolutionized the way we work, live, and interact with the world. Equipped with sensing, computing, networking, and communication capabilities, these devices are able to collect, analyze and transmit a wide range of data including images, videos, audio, texts, wireless signals, physiological signals from individuals and the physical world. In recent years, advancements in Artificial Intelligence (AI), particularly in deep learning (DL)/deep neural network (DNN), foundation models, and Generative AI, have propelled the integration of AI with IoT, making the concept of Artificial Intelligence of Things (AIoT) a reality. The synergy between IoT and modern AI enhances decision making, improves human-machine interactions, and facilitates more efficient operations, making AIoT one of the most exciting and promising areas that have the potential to fundamentally transform how people perceive and interact with the world. As illustrated in Figure 1, at its core, AIoT is grounded on three key components: sensing, computing, and networking & communication.

This paper intends to address the challenge of personalized recipe recommendation in the realm of diverse culinary preferences. The problem domain involves recipe recommendations, utilizing techniques such as association analysis and classification. Association analysis explores the relationships and connections between different ingredients to enhance the user experience. Meanwhile, the classification aspect involves categorizing recipes based on user-defined ingredients and preferences. A unique aspect of the paper is the consideration of recipes and ingredients belonging to multiple classes, recognizing the complexity of culinary combinations. This necessitates a sophisticated approach to classification and recommendation, ensuring the system accommodates the nature of recipe categorization. The paper seeks not only to recommend recipes but also to explore the process involved in achieving accurate and personalized recommendations.

Every fingerprint is believed to be unique, making it possible to identify an individual by matching a new fingerprint with an image on file, whether to unlock a mobile phone, access a bank account, or solve a murder. Fingerprint examiners, however, do not always agree on whether two print images match and, asked to recheck their work after several months, they sometimes do not even agree with themselves. That is leading to increased use of neural networks, powerhouses for identifying and matching patterns of all sorts, to automate and improve decisions about whether two fingerprints come from the same person. A group of computer scientists decided to use neural networks to test the assumption that no two fingerprints are the same. Using twin neural networks, researchers from Columbia University, Tufts University, and the State University of New York (SUNY) University at Buffalo looked for similarities between different fingerprints in a database from the National Institute of Standards and Technology (NIST).

It takes a village: Supporting inquiry- and equity-oriented computer science pedagogy through a professional learning community.

Directed evolution is a molecular biology technique that is transforming protein engineering by creating proteins with desirable properties and functions. However, it is experimentally impossible to perform the deep mutational scanning of the entire protein library due to the enormous mutational space, which scales as $20^N$ , where N is the number of amino acids. This has led to the rapid growth of AI-assisted directed evolution (AIDE) or AI-assisted protein engineering (AIPE) as an emerging research field. Aided with advanced natural language processing (NLP) techniques, including long short-term memory, autoencoder, and transformer, sequence-based embeddings have been dominant approaches in AIDE and AIPE. Persistent Laplacians, an emerging technique in topological data analysis (TDA), have made structure-based embeddings a superb option in AIDE and AIPE. We argue that a class of persistent topological Laplacians (PTLs), including persistent Laplacians, persistent path Laplacians, persistent sheaf Laplacians, persistent hypergraph Laplacians, persistent hyperdigraph Laplacians, and evolutionary de Rham-Hodge theory, can effectively overcome the limitations of the current TDA and offer a new generation of more powerful TDA approaches. In the general framework of topological deep learning, mathematics-assisted directed evolution (MADE) has a great potential for future protein engineering.

Rep. Bill Huizenga, R-Mich., joined'Fox & Friends First' to discuss the latest details surrounding the fatal shooting at Michigan State University and an upcoming briefing on the flying objects in U.S. airspace. Members of the Vanderbilt staff apologized on Friday for using ChatGPT, an artificial intelligence (AI) generator, to send an email to students calling for the community to come together following the shooting at Michigan State University. The email was sent on Thursday by the Peabody Office of Equity, Diversity, and Inclusion (EDI) at the university's Peabody College and included a note at the bottom that indicated the email had been written using ChatGPT, Vanderbilt's official student newspaper, The Vanderbilt Hustler, first reported on Friday. Associate Dean Nicole Joseph sent another email on Friday and said using ChatGPT to write the email was "poor judgment," according to the Hustler. OpenAI ChatGPT seen on mobile with AI Brain seen on screen.

A joint research collaboration between US universities and IBM has formulated a proof-of-concept adversarial attack that's theoretically capable of causing stock market losses, simply by changing one word in a retweet of a Twitter post. In one experiment, the researchers were able to hobble the Stocknet prediction model with two methods: a manipulation attack and a concatenation attack. The attack surface for an adversarial attack on automated and machine learning stock prediction systems is that a growing number of them are relying on organic social media as predictors of performance; and that manipulating this'in-the-wild' data is a process that can, potentially, be reliably formulated. Besides Twitter, systems of this nature ingest data from Reddit, StockTwits, and Yahoo News, among others. The difference between Twitter and the other sources is that retweets are editable, even if the original tweets are not.

Neither technologies nor societies are neutral, and failing to acknowledge this, results at best, in a narrow view of both. At worst, it leads to technology that reinforces oppressive societal norms. We agree with Alex Hanna, Timnit Gebru, and others who argue individual harms reflect institutional problems, and thus require institutional and systemic solutions. We believe computer science (CS) as a discipline often promotes itself as objective and neutral. This tendency allows the field to ignore systems of oppression that exist within and because of CS. As scholars in educational psychology, computer science education, and social studies education, we suggest a way forward through institutional change, specifically in the way we teach CS.