Timnit Gebru never thought a scientific paper would cause her so much trouble. In 2020, as the co-lead of Google's ethical AI team, Gebru had reached out to Emily Bender, a linguistics professor at the University of Washington, and asked to collaborate on research about the troubling direction of artificial intelligence. Gebru wanted to identify the risks posed by large language models, one of the most stunning recent breakthroughs in AI research. The models are algorithms trained on staggering amounts of text. Under the right conditions, they can compose what look like convincing passages of prose.

If you use such social media websites as Facebook and Twitter, you may have come across posts flagged with warnings about misinformation. So far, most misinformation – flagged and unflagged – has been aimed at the general public. Imagine the possibility of misinformation – information that is false or misleading – in scientific and technical fields like cybersecurity, public safety and medicine. There is growing concern about misinformation spreading in these critical fields as a result of common biases and practices in publishing scientific literature, even in peer-reviewed research papers. As a graduate student and as faculty members doing research in cybersecurity, we studied a new avenue of misinformation in the scientific community.

Operations in many essential industries including finance and banking are often characterized by the need to perform repetitive sequential tasks. Despite their criticality to the business, workflows are rarely fully automated or even formally specified, though there may exist a number of natural language documents describing these procedures for the employees of the company. Plan extraction methods provide us with the possibility of extracting structure plans from such natural language descriptions of the plans/workflows, which could then be leveraged by an automated system. In this paper, we investigate the utility of generalized language models in performing such extractions directly from such texts. Such models have already been shown to be quite effective in multiple translation tasks, and our initial results seem to point to their effectiveness also in the context of plan extractions. Particularly, we show that GPT-3 is able to generate plan extraction results that are comparable to many of the current state of the art plan extraction methods.

Developing mechanisms that flexibly adapt dialog systems to unseen tasks and domains is a major challenge in dialog research. Neural models implicitly memorize task-specific dialog policies from the training data. We posit that this implicit memorization has precluded zero-shot transfer learning. To this end, we leverage the schema-guided paradigm, wherein the task-specific dialog policy is explicitly provided to the model. We introduce the Schema Attention Model (SAM) and improved schema representations for the STAR corpus. SAM obtains significant improvement in zero-shot settings, with a +22 F1 score improvement over prior work. These results validate the feasibility of zero-shot generalizability in dialog. Ablation experiments are also presented to demonstrate the efficacy of SAM.

In transfer learning, it is imperative to achieve strong alignment between a pre-trained model and a downstream task. Prior work has done this by proposing task-specific pre-training objectives, which sacrifices the inherent scalability of the transfer learning paradigm. We instead achieve strong alignment by simultaneously modifying both the pre-trained model and the formulation of the downstream task, which is more efficient and preserves the scalability of transfer learning. We present GenSF (Generative Slot Filling), which leverages a generative pre-trained open-domain dialog model for slot filling. GenSF (1) adapts the pre-trained model by incorporating inductive biases about the task and (2) adapts the downstream task by reformulating slot filling to better leverage the pre-trained model's capabilities. GenSF achieves state-of-the-art results on two slot filling datasets with strong gains in few-shot and zero-shot settings. We achieve a 9 F1 score improvement in zero-shot slot filling. This highlights the value of strong alignment between the pre-trained model and the downstream task.

Every day, researchers are marking new milestones in the technology sphere. Artificial intelligence is reaching unprecedented heights, taking humankind along with it. Artificial intelligence defines the ability of machines or models to think and learn from experience. Starting from smart home applications and delivery systems to giant robots in factories and robotic surgeon, everything in the digital era is powered by artificial intelligence and its sub-technologies. After the technology got congested with many achievements, researchers divided it into different types of artificial intelligence for their ease.

OpenAI presented GPT-3 in May 2020 in a paper titled Language Models are Few-Shot Learners. In July 2020, the company released a beta API for developers to play and the model became an AI-rockstar overnight. GPT-3 is the third version of a family of Generative Pre-Trained language models. Its main features are multitasking and meta-learning abilities. Being trained in an unsupervised way on 570GB of Internet text data, it's able to learn tasks it hasn't been trained on by seeing a few examples (few-shot). It can also learn from zero- and one-shot settings, but the performance is usually worse.

In order to develop artificial general intelligence (AGI), the sort of all-encompassing AI that we see in science fiction, we might need to merely sit back and let a simple algorithm develop on its own. Reinforcement learning, a kind of gamified AI architecture in which an algorithm "learns" to complete a task by seeking out preprogrammed rewards, could theoretically grow and learn so much that it breaks the theoretical barrier to AGI without any new technological developments, according to research published by the Google-owned DeepMind last month in the journal Artificial Intelligence and spotted by VentureBeat. While reinforcement learning is often overhyped within the AI field, it's interesting to consider that engineers could have already built all the tech needed for AGI and now simply need to let it loose and watch it grow. The kind of artificial intelligence that we encounter every day of our lives, whether it's machine learning or reinforcement learning, is narrow AI: an algorithm designed to accomplish a very specific task like predicting your Google search, spotting objects in a video feed, or mastering a video game. By contrast, AGI -- sometimes called human-level AI intelligence -- would be more along the lines of C-3PO from "Star Wars," in the sense that it could understand context, subtext, and social cues.

Travis DeShazo is, to paraphrase Cake's 2001 song "Comfort Eagle," building a religion. He is building it bigger. He is increasing the parameters. The results are fairly convincing, too, at least as far as synthetic scripture (his words) goes. "Not a god of the void or of chaos, but a god of wisdom," reads one message, posted on the @gods_txt Twitter feed for GPT-2 Religion A.I. "This is the knowledge of divinity that I, the Supreme Being, impart to you. When a man learns this, he attains what the rest of mankind has not, and becomes a true god. Another message, this time important enough to be pinned to the top of the timeline, proclaims: "My sayings are a remedy for all your biological ills.

Every day, researchers are marking new milestones in the technology sphere. Artificial intelligence is reaching unprecedented heights, taking humankind along with it. Artificial intelligence defines the ability of machines or models to think and learn from experience. Starting from smart home applications and delivery systems to giant robots in factories and robotic surgeon, everything in the digital era is powered by artificial intelligence and its sub-technologies. After the technology got congested with many achievements, researchers divided it into different types of artificial intelligence for their ease.