In the world of blockchains, NFT's and many other trending scenarios, Artificial Intelligence is the one which is used in every innovation in different kind of ways which makes it perfect for the world to grow. Artificial Intelligence has its presence in almost every field of work, from healthcare and Medical Imaging analysis to self-driving cars all the things are covered with AI. There is one more area where AI plays a very important role that is Security. When it comes to security the one thing arises in our mind is safety. Everyone wants its data and private documents to be in safe hands, there should not be any malware attacks to our data.

Originally published on Towards AI the World's Leading AI and Technology News and Media Company. If you are building an AI-related product or service, we invite you to consider becoming an AI sponsor. At Towards AI, we help scale AI and technology startups. Let us help you unleash your technology to the masses. The whole idea of the Internet of Things is to extend the capability of the Internet beyond computers and smartphones to electronic, mechanical devices, sensors, etc.

Engineers tend to see things in unambiguous terms, which some may call Black and White terms, such as a choice between right or wrong and good and bad. The consideration of ethics in AI is highly nuanced, with vast gray areas, making it challenging for AI software engineers to apply it in their work. That was a takeaway from a session on the Future of Standards and Ethical AI at the AI World Government conference held in-person and virtually in Alexandria, Va. this week. An overall impression from the conference is that the discussion of AI and ethics is happening in virtually every quarter of AI in the vast enterprise of the federal government, and the consistency of points being made across all these different and independent efforts stood out. "We engineers often think of ethics as a fuzzy thing that no one has really explained," stated Beth-Anne Schuelke-Leech, an associate professor, Engineering Management and Entrepreneurship at the University of Windsor, Ontario, Canada, speaking at the Future of Ethical AI session.

The fundamental goals of healthcare are to improve people's lives and to prevent and treat disease. We may think of technology and artificial intelligence as tools for diagnostic and medical advancements like robotic surgery, "smart" prosthetics, or vaccine rollout. But the industry is discovering how the use of AI prevents healthcare fraud, waste and abuse (FWA), how advanced data security and cybersecurity solutions prevent security breaches, and how biometrics and machine learning protect patient information. Hospital directors are grappling with national losses of $300 billion to FWA each year and the constant threat of ransomware attacks, privacy breaches and identity theft. The massive scale of pandemic-related healthcare digitization increased these risks, causing healthcare leaders to seek better solutions.

A new artificial intelligence algorithm has discovered over 300 previously unknown exoplanets in data gathered by a now-defunct exoplanet-hunting telescope. The Kepler Space Telescope, NASA's first dedicated exoplanet hunter, has observed hundreds of thousands of stars in the search for potentially habitable worlds outside our solar system. The calatog of potential planets it had compiled continues generating new discoveries even after the telescope's demise. Human experts analyze the data for signs of exoplanets. But a new algorithm called ExoMiner can now mimic that procedure and scour the catalog faster and more efficiently.

The computing device deployment explosion experienced in recent years, motivated by the advances of technologies such as Internet-of-Things (IoT) and 5G, has led to a global scenario with increasing cybersecurity risks and threats. Among them, device spoofing and impersonation cyberattacks stand out due to their impact and, usually, low complexity required to be launched. To solve this issue, several solutions have emerged to identify device models and types based on the combination of behavioral fingerprinting and Machine/Deep Learning (ML/DL) techniques. However, these solutions are not appropriated for scenarios where data privacy and protection is a must, as they require data centralization for processing. In this context, newer approaches such as Federated Learning (FL) have not been fully explored yet, especially when malicious clients are present in the scenario setup. The present work analyzes and compares the device model identification performance of a centralized DL model with an FL one while using execution time-based events. For experimental purposes, a dataset containing execution-time features of 55 Raspberry Pis belonging to four different models has been collected and published. Using this dataset, the proposed solution achieved 0.9999 accuracy in both setups, centralized and federated, showing no performance decrease while preserving data privacy. Later, the impact of a label-flipping attack during the federated model training is evaluated, using several aggregation mechanisms as countermeasure. Zeno and coordinate-wise median aggregation show the best performance, although their performance greatly degrades when the percentage of fully malicious clients (all training samples poisoned) grows over 50%.

We argue that a valuable perspective on when a model learns \textit{good} representations is that inputs that are mapped to similar representations by the model should be perceived similarly by humans. We use \textit{representation inversion} to generate multiple inputs that map to the same model representation, then quantify the perceptual similarity of these inputs via human surveys. Our approach yields a measure of the extent to which a model is aligned with human perception. Using this measure of alignment, we evaluate models trained with various learning paradigms (\eg~supervised and self-supervised learning) and different training losses (standard and robust training). Our results suggest that the alignment of representations with human perception provides useful additional insights into the qualities of a model. For example, we find that alignment with human perception can be used as a measure of trust in a model's prediction on inputs where different models have conflicting outputs. We also find that various properties of a model like its architecture, training paradigm, training loss, and data augmentation play a significant role in learning representations that are aligned with human perception.

The planning domain has experienced increased interest in the formal synthesis of decision-making policies. This formal synthesis typically entails finding a policy which satisfies formal specifications in the form of some well-defined logic. While many such logics have been proposed with varying degrees of expressiveness and complexity in their capacity to capture desirable agent behavior, their value is limited when deriving decision-making policies which satisfy certain types of asymptotic behavior in general system models. In particular, we are interested in specifying constraints on the steady-state behavior of an agent, which captures the proportion of time an agent spends in each state as it interacts for an indefinite period of time with its environment. This is sometimes called the average or expected behavior of the agent and the associated planning problem is faced with significant challenges unless strong restrictions are imposed on the underlying model in terms of the connectivity of its graph structure. In this paper, we explore this steady-state planning problem that consists of deriving a decision-making policy for an agent such that constraints on its steady-state behavior are satisfied. A linear programming solution for the general case of multichain Markov Decision Processes (MDPs) is proposed and we prove that optimal solutions to the proposed programs yield stationary policies with rigorous guarantees of behavior.

Recent text-to-image matching models apply contrastive learning to large corpora of uncurated pairs of images and sentences. While such models can provide a powerful score for matching and subsequent zero-shot tasks, they are not capable of generating caption given an image. In this work, we repurpose such models to generate a descriptive text given an image at inference time, without any further training or tuning step. This is done by combining the visual-semantic model with a large language model, benefiting from the knowledge in both web-scale models. The resulting captions are much less restrictive than those obtained by supervised captioning methods. Moreover, as a zero-shot learning method, it is extremely flexible and we demonstrate its ability to perform image arithmetic in which the inputs can be either images or text and the output is a sentence. This enables novel high-level vision capabilities such as comparing two images or solving visual analogy tests.

As AI-powered technologies proliferate in the enterprise, the term "explainable AI" (XAI) has entered mainstream vernacular. XAI is a set of tools, techniques, and frameworks intended to help users and designers of AI systems understand their predictions, including how and why the systems arrived at them. A June 2020 IDC report found that business decision-makers believe explainability is a "critical requirement" in AI. To this end, explainability has been referenced as a guiding principle for AI development at DARPA, the European Commission's High-level Expert Group on AI, and the National Institute of Standards and Technology. Startups are emerging to deliver "explainability as a service," like Truera, and tech giants such as IBM, Google, and Microsoft have open-sourced both XAI toolkits and methods.