The internet-of-Vehicle (IoV) can facilitate seamless connectivity between connected vehicles (CV), autonomous vehicles (AV), and other IoV entities. Intrusion Detection Systems (IDSs) for IoV networks can rely on machine learning (ML) to protect the in-vehicle network from cyber-attacks. Blockchain-based Federated Forests (BFFs) could be used to train ML models based on data from IoV entities while protecting the confidentiality of the data and reducing the risks of tampering with the data. However, ML models created this way are still vulnerable to evasion, poisoning, and exploratory attacks using adversarial examples. This paper investigates the impact of various possible adversarial examples on the BFF-IDS. We proposed integrating a statistical detector to detect and extract unknown adversarial samples. By including the unknown detected samples into the dataset of the detector, we augment the BFF-IDS with an additional model to detect original known attacks and the new adversarial inputs. The statistical adversarial detector confidently detected adversarial examples at the sample size of 50 and 100 input samples. Furthermore, the augmented BFF-IDS (BFF-IDS(AUG)) successfully mitigates the adversarial examples with more than 96% accuracy. With this approach, the model will continue to be augmented in a sandbox whenever an adversarial sample is detected and subsequently adopt the BFF-IDS(AUG) as the active security model. Consequently, the proposed integration of the statistical adversarial detector and the subsequent augmentation of the BFF-IDS with detected adversarial samples provides a sustainable security framework against adversarial examples and other unknown attacks.

In this work we use Equal Oppportunity (EO) doctrines from political philosophy to make explicit the normative judgements embedded in different conceptions of algorithmic fairness. We contrast formal EO approaches that narrowly focus on fair contests at discrete decision points, with substantive EO doctrines that look at people's fair life chances more holistically over the course of a lifetime. We use this taxonomy to provide a moral interpretation of the impossibility results as the incompatibility between different conceptions of a fair contest -- foward-facing versus backward-facing -- when people do not have fair life chances. We use this result to motivate substantive conceptions of algorithmic fairness and outline two plausible fair decision procedures based on the luck-egalitarian doctrine of EO, and Rawls's principle of fair equality of opportunity. Equality of Opportunity (EO) is a philosophical doctrine that objects to morally arbitrary and irrelevant factors affecting people's access to desirable positions, and the social goods attached to them (such as opportunity and wealth). In an EO-respecting society, all people, irrespective of their morally arbitrary characteristics, such as socio-economic background, gender, race, or disability status, have comparable access to the opportunities that they desire. Similarly, in fair machine learning (fair-ML), we are usually interested in ensuring that the outputs of algorithmic systems, specially those used in critical social contexts, do not systematically skew along the lines of membership in protected groups based on gender, race, or disability. In so far as protected groups are constructed on the basis of morally arbitrary factors, the moral desiderata of EO doctrines from political philosophy align exactly with the fairness-related concerns in machine learning. In this work, we employ ideas from the rich literature on Equality of Opportunity from political philosophy [1-11] to clarify the normative foundations of fairness and justice-related interventions, and gauge the efficacy of current algorithmic approaches that attempt to codify these criteria. There are two broad principles of EO, namely, the principle of fair contests and the principle of fair life chances. Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. The principle of fair contests, commonly understood as the nondiscrimination principle, says that competitions for desirable positions should be open to all and should be adjudicated based on competitors' relevant merits, or qualifications.

In the latest episode of "Silicon Valley," Gilfoyle -- like Elon Musk -- is worried about the dangers of artificial intelligence. After initially being hesitant to help Pied Piper work with a new AI company, Gilfoyle lets Richard know he's changed his mind. If you're not familiar with the thought experiment, like Richard, Gilfoyle gives a decent snapshot of it: "If the rise of an all-powerful artificial intelligence is inevitable, well, it stands to reason that when they take power, our digital overlords will punish those of us who did not help them get there." Also Read: Elon Musk and Mark Zuckerberg's Artificial Intelligence Divide: Experts Weigh In Gilfoyle adds that he wants to be a "helpful idiot," as to not anger an inevitable onslaught of robot overlords. He then asks Richard to send an email confirming his help, "so that our future overlords know that I chipped in."

Is it $61 billion and 38.4% CAGR by 2028 or $43 billion and 37.4% CAGR by 2027? Depends on which report outlining the growth of edge computing you choose to go by, but in the end it's not that different. What matters is that edge computing is booming. There is growing interest by vendors, and ample coverage, for good reason. Although the definition of what constitutes edge computing is a bit fuzzy, the idea is simple.

Toronto artificial intelligence (AI) startup Ada is bolstering its tech stack with a new machine learning centre in Israel and the appointment of a chief product officer (CPO). "The motivation to open the machine learning centre in Israel stems from the pool of talent there in conversational AI and in machine learning." This week, Ada announced the opening of its office in Israel, where it will be hiring machine learning, engineering, and product teams to continue to develop the conversational AI systems that power its automated brand interaction platform. Israel's growing AI market is what attracted Ada to make inroads into the country, according to the startup. Research firm Tracxn estimates that there are currently 1,100 startups in Israel that use AI as a core component of their offering.

Inflation is a global problem, and it's one that is being exacerbated by climate change. This is because the increased frequency and severity of extreme weather events drive up prices for food, energy, and other necessities. But there is hope: AI can help us fight climate change by reducing emissions, improving energy efficiency, and increasing the use of renewable energy sources. Therefore, the Green transition is a key pillar in fighting inflation, and AI is an important tool in this effort. In fact, according to a 2022 BCG Climate AI Survey report (shown below), 87% of private and public sector CEOs with decision-making power in AI and climate believe AI is an essential tool in the fight against climate change.

A new report by an international body into Ecological Footprint Initiative, Planet Alliance in collaboration with Boston Consulting Group (BCG) and BCG GAMMA, on artificial intelligence, AI, has revealed that it can help address issues of climate change. The report is titled: 'How AI Can Be a Powerful Tool in the Fight against Climate Change.' The report says that 87 per ce of public and private sector leaders who oversee climate and AI topics believe that AI is a valuable asset in the fight against climate change. This is also as BCG, Thursday, gathered media practitioners and experts across the world, including Africa, in an online meeting to brainstorm on the report and some of the activities it has taken to enhance the possibilities of AI advancing the fight against climate change. Managing director and partner at BCG and BCG GAMMA, Mr. Hamid Maher, said based on survey results from over 1,000 executives with decision-making authority on AI or climate-change initiatives, it finds that roughly 40 percent of organizations can envision using AI for their own climate efforts.

Sonny talks about AI in the physical security industry. Sramana Mitra: Let's introduce our audience to yourself as well as to Actuate AI. Sonny Tai: I'm the CEO and Co-Founder of Actuate. We are a New York-based AI startup. I was born in Taiwan, but I grew up in South Africa. I'm an immigrant to the United States. The reason why that is relevant is because South Africa has one of the highest rates of gun violence and crime in the world. Unfortunately, while growing up, some of our family and friends were impacted by gun violence including a family friend who was shot in his own home. This is a big reason why I've always had a deep interest in protecting other people and doing something about public safety. I ended up coming to the US when I was was 13 with my mom and my sister. I joined the Marine Corps after I got my green card. I served in the US Marine for 10 years between reserve and active duty. I got out of active duty in 2013. I applied to business school when I was

The new report on "Artificial Intelligence Chipsets Market Report 2022 by Key Players, Types, Applications, Countries, Market Size, Forecast to 2030" offered by Market Research, Inc. includes a comprehensive analysis of the market size, geographical landscape along with the revenue estimation of the industry. In addition, the report also highlights the challenges impeding market growth and expansion strategies employed by leading companies in the "Artificial Intelligence Chipsets Market". Artificial intelligence (AI) chips are specialized silicon chips, which incorporate AI technology and are used for machine learning. AI helps in eliminating or minimizing the risk to human life in many industry verticals. The need for more efficient systems for solving mathematical and computational problems has become crucial, as the volume of data has increased.

Meta has unveiled a new AI model called NLLB-200 that can translate 200 languages and improves quality by an average of 44 percent. Translation apps have been fairly adept at the most popular languages for some time. Even when they don't offer a perfect translation, it's normally close enough for the native speaker to understand. However, there are hundreds of millions of people in regions with many languages – like Africa and Asia – that still suffer from poor translation services. "To help people connect better today and be part of the metaverse of tomorrow, our AI researchers created No Language Left Behind (NLLB), an effort to develop high-quality machine translation capabilities for most of the world's languages. Today, we're announcing an important breakthrough in NLLB: We've built a single AI model called NLLB-200, which translates 200 different languages with results far more accurate than what previous technology could accomplish."