Decisions such as which movie to watch next, which song to listen to, or which product to buy online, are increasingly influenced by recommender systems and user models that incorporate information on users' past behaviours, preferences, and digitally created content. Machine learning models that enable recommendations and that are trained on user data may unintentionally leverage information on human characteristics that are considered vulnerabilities, such as depression, young age, or gambling addiction. The use of algorithmic decisions based on latent vulnerable state representations could be considered manipulative and could have a deteriorating impact on the condition of vulnerable individuals. In this paper, we are concerned with the problem of machine learning models inadvertently modelling vulnerabilities, and want to raise awareness for this issue to be considered in legislation and AI ethics. Hence, we define and describe common vulnerabilities, and illustrate cases where they are likely to play a role in algorithmic decision-making. We propose a set of requirements for methods to detect the potential for vulnerability modelling, detect whether vulnerable groups are treated differently by a model, and detect whether a model has created an internal representation of vulnerability. We conclude that explainable artificial intelligence methods may be necessary for detecting vulnerability exploitation by machine learning-based recommendation systems.

We study safe policy improvement (SPI) for partially observable Markov decision processes (POMDPs). SPI is an offline reinforcement learning (RL) problem that assumes access to (1) historical data about an environment, and (2) the so-called behavior policy that previously generated this data by interacting with the environment. SPI methods neither require access to a model nor the environment itself, and aim to reliably improve the behavior policy in an offline manner. Existing methods make the strong assumption that the environment is fully observable. In our novel approach to the SPI problem for POMDPs, we assume that a finite-state controller (FSC) represents the behavior policy and that finite memory is sufficient to derive optimal policies. This assumption allows us to map the POMDP to a finite-state fully observable MDP, the history MDP. We estimate this MDP by combining the historical data and the memory of the FSC, and compute an improved policy using an off-the-shelf SPI algorithm. The underlying SPI method constrains the policy-space according to the available data, such that the newly computed policy only differs from the behavior policy when sufficient data was available. We show that this new policy, converted into a new FSC for the (unknown) POMDP, outperforms the behavior policy with high probability. Experimental results on several well-established benchmarks show the applicability of the approach, even in cases where finite memory is not sufficient.

Deep Neural Networks (DNNs) and Spiking Neural Networks (SNNs) are both known for their susceptibility to adversarial attacks. Therefore, researchers in the recent past have extensively studied the robustness and defense of DNNs and SNNs under adversarial attacks. Compared to accurate SNNs (AccSNN), approximate SNNs (AxSNNs) are known to be up to 4X more energy-efficient for ultra-low power applications. Unfortunately, the robustness of AxSNNs under adversarial attacks is yet unexplored. In this paper, we first extensively analyze the robustness of AxSNNs with different structural parameters and approximation levels under two gradient-based and two neuromorphic attacks. Then, we propose two novel defense methods, i.e., precision scaling and approximate quantization-aware filtering (AQF), for securing AxSNNs. We evaluated the effectiveness of these two defense methods using both static and neuromorphic datasets. Our results demonstrate that AxSNNs are more prone to adversarial attacks than AccSNNs, but precision scaling and AQF significantly improve the robustness of AxSNNs. For instance, a PGD attack on AxSNN results in a 72\% accuracy loss compared to AccSNN without any attack, whereas the same attack on the precision-scaled AxSNN leads to only a 17\% accuracy loss in the static MNIST dataset (4X robustness improvement). Similarly, a Sparse Attack on AxSNN leads to a 77\% accuracy loss when compared to AccSNN without any attack, whereas the same attack on an AxSNN with AQF leads to only a 2\% accuracy loss in the neuromorphic DVS128 Gesture dataset (38X robustness improvement).

Non-independent and identically distributed (non-IID) data is a key challenge in federated learning (FL), which usually hampers the optimization convergence and the performance of FL. Existing data augmentation methods based on federated generative models or raw data sharing strategies for solving the non-IID problem still suffer from low performance, privacy protection concerns, and high communication overhead in decentralized tabular data. To tackle these challenges, we propose a federated tabular data augmentation method, named Fed-TDA. The core idea of Fed-TDA is to synthesize tabular data for data augmentation using some simple statistics (e.g., distributions of each column and global covariance). Specifically, we propose the multimodal distribution transformation and inverse cumulative distribution mapping respectively synthesize continuous and discrete columns in tabular data from a noise according to the pre-learned statistics. Furthermore, we theoretically analyze that our Fed-TDA not only preserves data privacy but also maintains the distribution of the original data and the correlation between columns. Through extensive experiments on five real-world tabular datasets, we demonstrate the superiority of Fed-TDA over the state-of-the-art in test performance and communication efficiency.

Scale AI, the San Francisco–based company that uses software and people to label image, text, voice and video data for companies building machine learning algorithms, laid off 20% of its workforce this week. The decision, which was announced by founder and CEO Alexandr Wang via a company blog post, was made after rapid hiring in 2021 and 2022 came crashing into present-day macroeconomic challenges. The company did not say how many people work at Scale AI. However, back in February 2022, the company told TechCrunch it employed about 450 people. Scale AI, which was last valued at $7.3 billion and is backed by a slew of investors such as Tiger Global, Coatue Management and Founders Fund, has been a rising star in the AI industry.

A new academic program developed at MIT aims to teach U.S. Air and Space Forces personnel to understand and utilize artificial intelligence technologies. In a recent peer-reviewed study, the program researchers found that this approach was effective and well-received by employees with diverse backgrounds and professional roles. The project, which was funded by the Department of the Air Force–MIT Artificial Intelligence Accelerator, seeks to contribute to AI educational research, specifically regarding ways to maximize learning outcomes at scale for people from a variety of educational backgrounds. Experts in MIT Open Learning built a curriculum for three general types of military personnel -- leaders, developers, and users -- utilizing existing MIT educational materials and resources. They also created new, more experimental courses that were targeted at Air and Space Forces leaders.

Only weeks after a Southwest Airlines meltdown led to thousands of canceled flights and stranded passengers, the nation's air travel system was briefly interrupted Wednesday due to an outage in the computer system used by the Federal Aviation Administration to give pilots vital information before they take off. While the FAA system was back online within hours and flights were slowly returning to schedule, those passengers whose lives were upended in last month's Southwest debacle are still feeling the effects of the meltdown and tallying up the financial damage they endured. Passengers who spoke to The Times said the fiasco cost them between $700 in one instance (for gas costs) and $70,000 in another (for a destination wedding that was ruined). "I am trying to be patient and give them a chance to make things right," said one of Southwest's stranded passengers, actor Deborah Rombaut. "What bothers me is that I don't have a timeline as far as when I'll be reimbursed." Thousands of holiday travelers like Rombaut were stranded late last month when Southwest Airlines said its computer system that tracked crew scheduling could not keep up with a severe winter storm.

Autonomous intelligence, artificial intelligence (AI) that can act without human intervention, can help identify critical infrastructure cyberattack patterns and network activity, and detect malware to enable enhanced decision-making about defensive responses. That's according to the preliminary findings of an international experiment of AI's ability to secure and defend systems, power grids and other critical assets by cyber experts at the North Atlantic Treaty Organization's (NATO) Cyber Coalition 2022 event late last year. The simulated experiment saw six teams of cyber defenders from NATO allies tasked with setting up computer-based systems and power grids at an imaginary military base and keeping them running during a cyberattack. If hackers interfered with system operations or the power went down for more than 10 minutes, critical systems could go offline. The differentiator was that three of the teams had access to a novel Autonomous Intelligence Cyberdefense Agent (AICA) prototype developed by the US Department of Energy's (DOE) Argonne National Laboratory, while the other three teams did not.

ChatGPT, the popular conversational AI model capable of mimicking human responses, has been updated with improved accuracy. Upon opening the interface the ChatGPT interface, users will be greeted with a new pop-up message that lists the changes in what OpenAI calls the "Jan 9 version" update. Provided by The Indian Express chatgpt update OpenAI says that the latest update brings the ability to stop generating ChatGPT's response (Express photo) It should be generally better across a wide range of topics and has improved factuality. The first point could pertain to ChatGPT's potential to spread misinformation. While the chatbot does have built-in functions to help it avoid offensive responses and factual errors, it's still far from perfect. OpenAI has also admitted that ChatGPT is susceptible to providing "plausible-sounding but incorrect or nonsensical answers."

The consortium of SPATIAL Project (Security and Privacy Accountable Technology Innovations, Algorithms, and Machine Learning) announces the official start of this joint European initiative funded by the European Commission under the Horizon 2020 Research & Innovation programme. Get to know about the project, the partners and the use cases. SPATIAL planning to participate at the IoT Week 2022. Learn more about the partners through the intereviews and get to know how SPATIAL participated at the EuCNC in Grenoble (June 2022). "Digital Services Act and Digital Markets Act set a new cornerstone for digital in Europe" article.