The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capability. As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations. This large-scale dataset and its promptable nature endow the model with strong zero-shot generalization. Although the SAM has shown competitive performance on several datasets, we still want to investigate its zero-shot generalization on medical images. As we know, the acquisition of medical image annotation usually requires a lot of effort from professional practitioners. Therefore, if there exists a foundation model that can give high-quality mask prediction simply based on a few point prompts, this model will undoubtedly become the game changer for medical image analysis. To evaluate whether SAM has the potential to become the foundation model for medical image segmentation tasks, we collected more than 12 public medical image datasets that cover various organs and modalities. We also explore what kind of prompt can lead to the best zero-shot performance with different modalities. Furthermore, we find that a pattern shows that the perturbation of the box size will significantly change the prediction accuracy. Finally, Extensive experiments show that the predicted mask quality varied a lot among different datasets. And providing proper prompts, such as bounding boxes, to the SAM will significantly increase its performance.

This paper presents the FlowTransformer framework, a novel approach for implementing transformer-based Network Intrusion Detection Systems (NIDSs). FlowTransformer leverages the strengths of transformer models in identifying the long-term behaviour and characteristics of networks, which are often overlooked by most existing NIDSs. By capturing these complex patterns in network traffic, FlowTransformer offers a flexible and efficient tool for researchers and practitioners in the cybersecurity community who are seeking to implement NIDSs using transformer-based models. FlowTransformer allows the direct substitution of various transformer components, including the input encoding, transformer, classification head, and the evaluation of these across any flow-based network dataset. To demonstrate the effectiveness and efficiency of the FlowTransformer framework, we utilise it to provide an extensive evaluation of various common transformer architectures, such as GPT 2.0 and BERT, on three commonly used public NIDS benchmark datasets. We provide results for accuracy, model size and speed. A key finding of our evaluation is that the choice of classification head has the most significant impact on the model performance. Surprisingly, Global Average Pooling, which is commonly used in text classification, performs very poorly in the context of NIDS. In addition, we show that model size can be reduced by over 50\%, and inference and training times improved, with no loss of accuracy, by making specific choices of input encoding and classification head instead of other commonly used alternatives.

The deployment of autonomous robots in various domains has raised significant concerns about their trustworthiness and accountability. This study explores the potential of Large Language Models (LLMs) in analyzing ROS 2 logs generated by autonomous robots and proposes a framework for log analysis that categorizes log files into different aspects. The study evaluates the performance of three different language models in answering questions related to StartUp, Warning, and PDDL logs. The results suggest that GPT 4, a transformer-based model, outperforms other models, however, their verbosity is not enough to answer why or how questions for all kinds of actors involved in the interaction.

Twitter CEO Elon Musk provides insight on the consequences of developing artificial intelligence and the potential impact on elections on'Tucker Carlson Tonight.' Academic leaders from around the world penned an open letter calling on artificial intelligence developers to learn more about consciousness as artificial intelligence (AI) systems advance rapidly, giving it a prominent place in our moral landscape, raising ethnical, legal and political concerns. The Association for Mathematical Consciousness Science (AMCS), "a large community of over 150 international researchers who are spearheading mathematical and computational approaches to consciousness," published a letter Wednesday as "a wakeup call for the tech sector, the scientific community and society in general to take seriously the need to accelerate research in the field of consciousness science." The Association for Mathematical Consciousness Science published an open letter calling on "the tech sector, the scientific community and society in general to take seriously the need to accelerate research in the field of consciousness science." Its writers referenced the recent letter written by leaders in tech that called for a pause in AI experiments, noting "we are living through an exciting and uncertain time in the development of artificial intelligence (AI) and other brain-related technologies" and warned that AI is "accelerating at a pace that far exceeds our progress in understanding their capabilities and their'alignment' with human values." Signatories of the letter argue that language models like OpenAI's ChatGPT and Google's Bard are based on the neural networks of animal brains, but in the near future will be constructed to mimic "aspects of higher-level brain architecture and functioning."

Such an obligation would give publishers and content creators a new weapon to seek a share of profits when their works are used as source material for AI-generated content by tools like ChatGPT. The issue has been one of the thorniest commercial questions to emerge amid a frenzy of AI-powered tools being launched or tested by the likes of Microsoft Corp. and Google owner Alphabet Inc.

Fox News correspondent Grady Trimble has the latest on fears the technology will spiral out of control on'Special Report.' The use of artificial intelligence among Americans has skyrocketed since the release of platforms such as ChatGPT, and a new study found that residents of states out West are far more likely to use AI than Southern states. "The use of Artificial Intelligence in the US is on the rise, and it's clear to see why," a spokesperson for YACSS, an AI-driven company that builds websites and also conducted the study, said of the findings in a report provided to Fox News Digital. "It is frequently used to reduce time spent on tedious tasks as well as provide users with endless creative possibilities, and this is all available at the touch of a button." The study, released this month, examined Google data on keywords frequently searched by people interested in artificial intelligence over a 12-month span, and averaged each state's monthly search volume for such terms per 100,000 people.

Elon Musk caused a stir last week when he told the (recently fired) right-wing provocateur Tucker Carlson that he plans to build "TruthGPT," a competitor to OpenAI's ChatGPT. Musk says the incredibly popular bot displays "woke" bias and that his version will be a "maximum truth-seeking AI"--suggesting only his own political views reflect reality. Musk is far from the only person worried about political bias in language models, but others are trying to use AI to bridge political divisions rather than push particular viewpoints. David Rozado, a data scientist based in New Zealand, was one of the first people to draw attention to the issue of political bias in ChatGPT. Several weeks ago, after documenting what he considered liberal-leaning answers from the bot on issues including taxation, gun ownership, and free markets, he created an AI model called RightWingGPT that expresses more conservative viewpoints.

Whether or not this really amounts to an "iPhone moment" or a serious threat to Google search isn't obvious at present -- while it will likely push a change in user behaviors and expectations, the first shift will be organizations pushing to bring tools trained on large language models (LLMs) to learn from their own data and services. And this, ultimately, is the key -- the significance and value of generative AI today is not really a question of societal or industry-wide transformation. It's instead a question of how this technology can open up new ways of interacting with large and unwieldy amounts of data and information. OpenAI is clearly attuned to this fact and senses a commercial opportunity: although the list of organizations taking part in the ChatGPT plugin initiative is small, OpenAI has opened up a waiting list where companies can sign up to gain access to the plugins. In the months to come, we will no doubt see many new products and interfaces backed by OpenAI's generative AI systems.

LinkedIn influencers use ChatGPT as a brainstorming aid, should you? OpenAI's chatbot responds in a conversational tone to text prompts, and millions of users continue to experiment with it. The chatbot helps software developers with coding, scientists with research, and students with homework. With a little repetition and exploration, ChatGPT is worth trying out as part of your brainstorming process. Business leaders can use it to consider multiple approaches for crucial conversations or long-term decisions.

Welcome to our April 2023 monthly digest, where you can catch up with any AIhub stories you may have missed, peruse the latest news, find out about recent events, and more. This month, we learn how to address class imbalance in natural language processing, investigate personalized reward functions, and put together a list of large language model resources. Class imbalance in training and evaluation datasets can pose a challenge for natural language processing (NLP) models, which are more heavily influenced by majority class data during training. As a result, NLP models tend to perform poorly on the minority classes, which often contain the cases that are most interesting to the downstream user. In this blogpost, Sophie Henning and Annemarie Friedrich give an overview of such class imbalance and survey methods for addressing it.