ChatGPT is OpenAI's new artificial–intelligence chatbot, and apparently it's becoming more common for both high school and college students to use …

Are you smarter than artificial intelligence? A new study finds one revolutionary program is putting human intellect to shame. Researchers from UCLA have found that the autoregressive language model Generative Pre-trained Transformer 3 (GPT-3) clearly outperforms the average college student in a series of reasoning tests that measure intelligence. The program uses deep learning to produce human-like text. GPT-3, a technology created by OpenAI, has a host of applications, including language translation and generating text for applications such as chatbots.

Pseudo Artificial Intelligence bias (PAIB) is broadly disseminated in the literature, which can result in unnecessary AI fear in society, exacerbate the enduring inequities and disparities in access to and sharing the benefits of AI applications, and waste social capital invested in AI research. This study systematically reviews publications in the literature to present three types of PAIBs identified due to: a) misunderstandings, b) pseudo mechanical bias, and c) over-expectations. We discussed the consequences of and solutions to PAIBs, including certifying users for AI applications to mitigate AI fears, providing customized user guidance for AI applications, and developing systematic approaches to monitor bias. We concluded that PAIB due to misunderstandings, pseudo mechanical bias, and over-expectations of algorithmic predictions is socially harmful.

Recent advances in Artificial Intelligence (AI) have sparked renewed interest in its potential to improve education. However, AI is a loose umbrella term that refers to a collection of methods, capabilities, and limitations-many of which are often not explicitly articulated by researchers, education technology companies, or other AI developers. In this paper, we seek to clarify what "AI" is and the potential it holds to both advance and hamper educational opportunities that may improve the human condition. We offer a basic introduction to different methods and philosophies underpinning AI, discuss recent advances, explore applications to education, and highlight key limitations and risks. We conclude with a set of questions that educationalists may ask as they encounter AI in their research and practice. Our hope is to make often jargon-laden terms and concepts accessible, so that all are equipped to understand, interrogate, and ultimately shape the development of human centered AI in education.

Spiking neural networks (SNNs) are promising brain-inspired energy-efficient models. Recent progress in training methods has enabled successful deep SNNs on large-scale tasks with low latency. Particularly, backpropagation through time (BPTT) with surrogate gradients (SG) is popularly used to achieve high performance in a very small number of time steps. However, it is at the cost of large memory consumption for training, lack of theoretical clarity for optimization, and inconsistency with the online property of biological learning and rules on neuromorphic hardware. Other works connect spike representations of SNNs with equivalent artificial neural network formulation and train SNNs by gradients from equivalent mappings to ensure descent directions. But they fail to achieve low latency and are also not online. In this work, we propose online training through time (OTTT) for SNNs, which is derived from BPTT to enable forward-in-time learning by tracking presynaptic activities and leveraging instantaneous loss and gradients. Meanwhile, we theoretically analyze and prove that gradients of OTTT can provide a similar descent direction for optimization as gradients based on spike representations under both feedforward and recurrent conditions. OTTT only requires constant training memory costs agnostic to time steps, avoiding the significant memory costs of BPTT for GPU training. Furthermore, the update rule of OTTT is in the form of three-factor Hebbian learning, which could pave a path for online on-chip learning. With OTTT, it is the first time that two mainstream supervised SNN training methods, BPTT with SG and spike representation-based training, are connected, and meanwhile in a biologically plausible form. Experiments on CIFAR-10, CIFAR-100, ImageNet, and CIFAR10-DVS demonstrate the superior performance of our method on large-scale static and neuromorphic datasets in small time steps.

The ability for an agent to continuously learn new skills without catastrophically forgetting existing knowledge is of critical importance for the development of generally intelligent agents. Most methods devised to address this problem depend heavily on well-defined task boundaries, and thus depend on human supervision. Our task-agnostic method, Self-Activating Neural Ensembles (SANE), uses a modular architecture designed to avoid catastrophic forgetting without making any such assumptions. At the beginning of each trajectory, a module in the SANE ensemble is activated to determine the agent's next policy. During training, new modules are created as needed and only activated modules are updated to ensure that unused modules remain unchanged. This system enables our method to retain and leverage old skills, while growing and learning new ones. We demonstrate our approach on visually rich procedurally generated environments.

In this contributed article, Ankush Gupta and Kavya Shree of FischerJordan, explore the scope, use cases and challenges of AutoML and how data scientists and AutoML can have a future together. The authors discuss the causes driving the use of AutoML, the benefits and challenges associated, and major providers in the space. They conclude by analyzing the parts of the data science and ML process that can/cannot be automated and if AutoML will replace data scientists / both will go hand-in-hand.

Artificial intelligence, or AI, has the potential to revolutionize many aspects of our society, but it also has the potential to be harmful. As AI becomes more advanced and widespread, it raises ethical concerns about how it will impact jobs, privacy and inequality. Overall, it is important to carefully consider the potential drawbacks of AI and take steps to mitigate them. Would it surprise you if I said that the above paragraph was written entirely by AI? It's true -- using the newish AI called ChatGPT, I typed in the following query: "Write me an opening paragraph about the dangers of artificial intelligence." ChatGPT was developed by OpenAI, a startup founded in part by Big Tech mogul Elon Musk.

Check out all the on-demand sessions from the Intelligent Security Summit here. Plagiarism isn't limited to essays. Programming plagiarism -- where a developer copies code deliberately without attribution -- is an increasing trend. According to a New York Times article, at Brown University, more than half of the 49 allegations of academic code violations in 2016 involved cheating in computer science. At Stanford, as many as 20% of the students in a single 2015 computer science course were flagged for possible cheating, the same piece reports.

As the final year of college approached, my friends and I, all electronics engineering students at Sudan University of Science and Technology, found ourselves struggling to come up with ideas for our graduation project. One day, while sitting in what we called "Jabnah" a version of a local cafe here is a picture of what it looks like I suggested using deep learning for malaria detection. My friends were skeptical, as we knew nothing about either deep learning or malaria. But I was determined to learn and proposed that we take an online course on deep learning. The course was challenging, but I was determined to succeed.