The presenter pasted in what he called "about 16 pages' worth of tax code" These seven sentences about Alice, Bob, and Charlie come word-for-word from a handcrafted data set we developed at Johns Hopkins University and published in 2020 for training and measuring AI models for reasoning over statutory language. Every word, punctuation mark, and Maryland; Nils number in the taxpayer facts comes exactly from Holzenberger is an our tax_case_9 -- even the percent sign at the start associate professor in of the line. This work has been supported by the U.S. National Science Foundation under grant No. 2204926. The entire livestream is available at OpenAI, "GPT-4 Developer The tax law example starts at minute 19:11. Go to the directory "Cases" to find the file tax_case_9.pl. Tax_case_9.pl is written in the programming language Prolog. Federal content, please visit www.taxnotes.com. Where did the "about 16 pages' worth of tax out the TCJA standard deduction increase at code" come from? Again, from our 2020 data set. SARA has two deduction for 2018 was $24,000. From minute 20:07 to 20:40 of the livestream, handcrafted cases in SARA; tax_case_9 is one of we see some of the tax sections pasted into GPT-4. The statutes consist of nine sections of the These are SARA's heavily edited version of the IRC, For example, at and remove ambiguity. If you put all the SARA 20:23, we see part of section 63(c) with the statutes into a single file it will be about 16 pages paragraphs jumping from (3) to (5); in SARA, we long (depending on the font). At 20:26, we see part of section One of our edits was paring section 1 down to 63(c)(6) with only subparagraphs (A), (B), and (D); only sections 1(a) through (d), which contain the in SARA, we edited out (C). At 20:40, we see parts Clinton-era tax rates. We cut section 1(j), which of section 3306(b) with the paragraphs jumping contains the reduced Tax Cuts and Jobs Act rates from (2) to (7); in SARA, we edited out paragraphs for 2018-2025. This editing explains why GPT-4 (3) through (6). At 20:39 we see sections 3301 and got the wrong answer on the livestream for Alice 3306 regarding the federal unemployment tax; and Bob's 2018 taxes. We did not, however, edit while these two sections are irrelevant to Alice and Bob's tax liability in tax_case_9, they are two The author Holzenberger did all the handcrafting and hand editing. Federal content, please visit www.taxnotes.com. You can We empirically verified that using the SARA download our data set and compare it with the version of the IRC causes GPT-4 to get the wrong livestream's recording on YouTube. First, we The presenter then gives directions to GPT-4: pasted into GPT-4 all nine SARA statutes, plus our "Now calculate their total liability." GPT-4 gives facts about Alice, Bob, and Charlie. Then we detailed step-by-step calculations and concludes used the same "Now calculate their total liability" that "Alice and Bob's total tax liability for 2018 is command.

Science fiction, when revisited years later, sometimes doesn't come across as all that fictional. Speculative novels have an impressive track record at prophesying what innovations are to come, and how they might upend the world: H. G. Wells wrote about an atomic bomb decades before World War II, and Ray Bradbury's 1953 novel, Fahrenheit 451, features devices we'd describe today as Bluetooth earbuds. Perhaps no writer has been more clairvoyant about our current technological age than Neal Stephenson. His novels coined the term metaverse, laid the conceptual groundwork for cryptocurrency, and imagined a geoengineered planet. A core element of one of his early novels, The Diamond Age: Or, a Young Lady's Illustrated Primer, is a magical book that acts as a personal tutor and mentor for a young girl, adapting to her learning style--in essence, it is a personalized and ultra-advanced chatbot.

Meta plans to ramp up its labeling of AI-generated images across Facebook, Instagram and Threads to help make it clear that the visuals are artificial. It's part of a broader push to tamp down misinformation and disinformation, which is particularly significant as we wrangle with the ramifications of generative AI (GAI) in a major election year in the US and other countries. According to Meta's president of global affairs, Nick Clegg, the company has been working with partners from across the industry to develop standards that include signifiers that an image, video or audio clip has been generated using AI. "Being able to detect these signals will make it possible for us to label AI-generated images that users post to Facebook, Instagram and Threads," Clegg wrote in a Meta Newsroom post. "We're building this capability now, and in the coming months we'll start applying labels in all languages supported by each app."

We've collected some of the articles, opinion pieces, videos and resources relating to generative AI models. We periodically update this list to add further resources of interest. This article represents the fourth in the series.

Meta, like other leading tech companies, has spent the past year promising to speed up deployment of generative artificial intelligence. Today it acknowledged it must also respond to the technology's hazards, announcing an expanded policy of tagging AI-generated images posted to Facebook, Instagram, and Threads with warning labels to inform people of their artificial origins. Yet much of the synthetic media likely to appear on Meta's platforms is unlikely to be covered by the new policy, leaving many gaps through which malicious actors could slip. "It's a step in the right direction, but with challenges," says Sam Gregory, program director of the nonprofit Witness, which helps people use technology to support human rights. Meta already labels AI-generated images made using its own generative AI tools with the tag "Imagined with AI," in part by looking for the digital "watermark" its algorithms embed into their output.

With artificial intelligence, creative barriers have been lowered. There are myriad platforms today that can help you bring your ideas to life in just a few clicks. In The Complete Generative AI Art & Design Mastery Bundle, you'll learn how to master a few of these leading platforms. This 7-course bundle includes more than 14 hours of training geared towards platforms like Midjourney, DALL-E, and Stable Diffusion. You'll learn how these platforms work and how to tailor settings to get the outputs that you want.

The industry ministry said on Tuesday it would extend subsidies worth as much as 242.9 billion ( 1.64 billion) for Bain Capital-backed Kioxia and Western Digital to expand memory chip production in Mie and Iwate prefectures. The funding provides underpinning for the two companies, which have been hammered by a slump in the market for NAND flash chips and whose merger talks stalled late last year following opposition from Kioxia investor SK Hynix. Japan's powerful industry ministry aims to reclaim the country's lost position as a major chip center by extending subsidies to domestic and foreign chipmakers and secure chip supply amid trade tensions between China and the United States. "The memory market is expected to grow significantly in the future, including for generative AI (artificial intelligence)," industry minister Ken Saito told reporters. "The joint investment by Kioxia and Western Digital brings together Japan and the U.S. to fulfill our responsibility to supply the memory the world needs," he said.

We explore the AI2050 "hard problems" that block the promise of AI and cause AI risks: (1) developing general capabilities of the systems; (2) assuring the performance of AI systems and their training processes; (3) aligning system goals with human goals; (4) enabling great applications of AI in real life; (5) addressing economic disruptions; (6) ensuring the participation of all; (7) at the same time ensuring socially responsible deployment; (8) addressing any geopolitical disruptions that AI causes; (9) promoting sound governance of the technology; and (10) managing the philosophical disruptions for humans living in the age of AI. For each problem, we outline the area, identify significant recent work, and suggest ways forward. [Note: this paper reviews literature through January 2023.]

The latest development in artificial intelligence (AI), chatbots, the product of generative AI, has captivated the public in the last two years. But it similarly poses an unprecedented challenge and can have potentially unwanted effects on our lives. OpenAI released the chatbot ChatGPT on November 30, 2022. The overwhelming response of the public towards ChatGPT usage pushed Google to release Bard, ChatGPT's rival, and Microsoft to release AI-powered Bing. But the recent GPT-4 topped the list as it has more capabilities than any other existing chatbot. Being LLM-based, these chatbots create synthetic media with the intention of creating better content, enhanced quality, or professional voices. The capabilities of such chatbots raise questions on the ethical use of AI. In the meantime, deepfakes, which are high-quality AI-generated fake videos, have been circulating online. Synthetically generated deepfake videos have exceeded acceptable limits in terms of reality distortion.

The advent of generative AI images has completely disrupted the art world. Distinguishing AI generated images from human art is a challenging problem whose impact is growing over time. A failure to address this problem allows bad actors to defraud individuals paying a premium for human art and companies whose stated policies forbid AI imagery. It is also critical for content owners to establish copyright, and for model trainers interested in curating training data in order to avoid potential model collapse. There are several different approaches to distinguishing human art from AI images, including classifiers trained by supervised learning, research tools targeting diffusion models, and identification by professional artists using their knowledge of artistic techniques. In this paper, we seek to understand how well these approaches can perform against today's modern generative models in both benign and adversarial settings. We curate real human art across 7 styles, generate matching images from 5 generative models, and apply 8 detectors (5 automated detectors and 3 different human groups including 180 crowdworkers, 4000+ professional artists, and 13 expert artists experienced at detecting AI). Both Hive and expert artists do very well, but make mistakes in different ways (Hive is weaker against adversarial perturbations while Expert artists produce higher false positives). We believe these weaknesses will remain as models continue to evolve, and use our data to demonstrate why a combined team of human and automated detectors provides the best combination of accuracy and robustness.