We are partnering with Learn Prompting in order to help build and spread how to do prompting and become better prompt engineers, which we believe will become more and more popular, and people will even be hired for this role in the near future (do you think prompt engineering will be a real job? We plan on covering the A to Z of prompting, including great applied and comprehensive tutorials for all the large and *hot* models, as well as a fun competition coming up towards the end of the year. Learn more about this new course and stay tuned for the fun competition with prizes -- including money -- in our new #learn-prompting channel on Discord! We will use this new channel to share announcements and answer questions related to Learn Prompting. You can also reach out to @Trigaten and me at any time.

Paula Arguello, Jhon Lopez, Carlos Hinojosa and Henry Arguello won the best paper award at the International Conference on Image Processing (ICIP) this year, for their work Optics lens design for privacy-preserving scene captioning. In this interview, Carlos tells us more about privacy-preserving scene captioning, how they approached the problem, and the key contributions of their work. We have digital cameras everywhere. They are fundamental to a range of intelligent systems that recognize relevant events and assist us in our daily activities. We have them in our cars, homes, hospitals, etc. However, their ever-improving ability to imitate the human vision system and produce the highest-quality images has raised concerns about privacy and security.

Daniele: Hi, Deep Learning Model; very lovely to meet you. Deep Learning Model: Hi Daniele, I cannot say it is a pleasure -- not sure what that means -- but this interaction is undoubtedly an outlier for me. But please call me 43420a6962c2. Daniele: Oh, ok, interesting name, I guess. Ok, 43420a6962c2, let's get cracking with this interview.

I knew ahead of time--how could I not? What I didn't anticipate was just why it would be so disturbing. In fact, no matter how hard I tried, I couldn't figure out how I died; the obituary didn't give any details beyond saying that I had passed away in April of last year. "Charles Seife's cause of death has not been released.") Nor did I find the attempt to summarize my entire life and personality into couple of sentences particularly disturbing.

Quantitative investment (``quant'') is an interdisciplinary field combining financial engineering, computer science, mathematics, statistics, etc. Quant has become one of the mainstream investment methodologies over the past decades, and has experienced three generations: Quant 1.0, trading by mathematical modeling to discover mis-priced assets in markets; Quant 2.0, shifting quant research pipeline from small ``strategy workshops'' to large ``alpha factories''; Quant 3.0, applying deep learning techniques to discover complex nonlinear pricing rules. Despite its advantage in prediction, deep learning relies on extremely large data volume and labor-intensive tuning of ``black-box'' neural network models. To address these limitations, in this paper, we introduce Quant 4.0 and provide an engineering perspective for next-generation quant. Quant 4.0 has three key differentiating components. First, automated AI changes quant pipeline from traditional hand-craft modeling to the state-of-the-art automated modeling, practicing the philosophy of ``algorithm produces algorithm, model builds model, and eventually AI creates AI''. Second, explainable AI develops new techniques to better understand and interpret investment decisions made by machine learning black-boxes, and explains complicated and hidden risk exposures. Third, knowledge-driven AI is a supplement to data-driven AI such as deep learning and it incorporates prior knowledge into modeling to improve investment decision, in particular for quantitative value investing. Moreover, we discuss how to build a system that practices the Quant 4.0 concept. Finally, we propose ten challenging research problems for quant technology, and discuss potential solutions, research directions, and future trends.

During the early years of the Cold War, an array of underwater microphones monitoring for sounds of Russian submarines captured something otherworldly in the depths of the North Atlantic. The haunting sounds came not from enemy craft, nor aliens, but humpback whales, a species that, at the time, humans had hunted almost to the brink of extinction. Years later, when environmentalist Roger Payne obtained the recordings from U.S. Navy storage and listened to them, he was deeply moved. The whale songs seemed to reveal majestic creatures that could communicate with one another in complex ways. If only the world could hear these sounds, Payne reasoned, the humpback whale might just be saved from extinction. When Payne released the recordings in 1970 as the album Songs of the Humpback Whale, he was proved right. It was played at the U.N. general assembly, and it inspired Congress to pass the 1973 endangered species act. By 1986, commercial whaling was banned under international law.

With the recent rise in popularity of text-to-image image generation engines, our friend Pratik Naik had a chat with one of the most popular A.I. chatbots, Open A.I. In his words, the conversation title is "I asked A.I. if humans have anything to fear when it comes to text to image A.I. generators? Here is what it said." It was an interesting conversation, although with a somber conclusion. We are bringing this interview as is and would love to hear if you are concerned about AI or consider it as an opportunity.

Using artificial intelligence, we learn what moments in MLB history were indeed the most famous and why.

AI chatbot experts are all talking about -- and talking to -- a newish research project from artificial intelligence research organization OpenAI. Conceptually, ChatGPT can be used like the AI art tools in the sense that minimal text input by the user produces credible synthetic media -- paragraphs instead of images. In fact, it can write convincing, often compelling essays, stories and even poems. And, like the AI image creators, you can direct ChatGPT to write prose in specific styles. I told ChatGPT to tell me about Twitter in three separate queries: one in the style of Ernest Hemingway, another in the style of Mark Twain and the third in the form of a limerick.

Laird Connectivity, a global leader in wireless modules, internal antennas, IoT Devices, and custom wireless solutions, is pleased to announce it has acquired California-based Boundary Devices, a leader in designing and manufacturing System-on-Modules (SOM) and Single Board Computers (SBC). Founded in 2003 and headquartered in Lake Forest, California, Boundary Devices is a pioneer and leader in providing innovative SOM and SBC products that serve a diverse and global customer base across high-growth end markets, including IoT, commercial equipment, laboratory instruments, and industrial automation. The Company provides a one-stop-shop destination for customers seeking a total-solutions partner able to offer: hardware design for NXP Semiconductors i.MX applications processors, software development, U.S. based manufacturing, and integration, all backed by best-in-class engineering and customer support. Recommended AI: How is Artificial Intelligence (AI) Changing the Future of Architecture? The acquisition significantly enhances the growing Laird Connectivity SOM portfolio by providing a full complement of SOM and SBC products for a wide range of customer applications.