In financial applications, regulations or best practices often lead to specific requirements in machine learning relating to four key pillars: fairness, privacy, interpretability and greenhouse gas emissions. These all sit in the broader context of sustainability in AI, an emerging practical AI topic. However, although these pillars have been individually addressed by past literature, none of these works have considered all the pillars. There are inherent trade-offs between each of the pillars (for example, accuracy vs fairness or accuracy vs privacy), making it even more important to consider them together. This paper outlines a new framework for Sustainable Machine Learning and proposes FPIG, a general AI pipeline that allows for these critical topics to be considered simultaneously to learn the trade-offs between the pillars better. Based on the FPIG framework, we propose a meta-learning algorithm to estimate the four key pillars given a dataset summary, model architecture, and hyperparameters before model training. This algorithm allows users to select the optimal model architecture for a given dataset and a given set of user requirements on the pillars. We illustrate the trade-offs under the FPIG model on three classical datasets and demonstrate the meta-learning approach with an example of real-world datasets and models with different interpretability, showcasing how it can aid model selection.

In the last 5 years, the availability of large audio datasets in African countries has opened unlimited opportunities to build machine intelligence (MI) technologies that are closer to the people and speak, learn, understand, and do businesses in local languages, including for those who cannot read and write. Unfortunately, these audio datasets are not fully exploited by current MI tools, leaving several Africans out of MI business opportunities. Additionally, many state-of-the-art MI models are not culture-aware, and the ethics of their adoption indexes are questionable. The lack thereof is a major drawback in many applications in Africa. This paper summarizes recent developments in machine intelligence in Africa from a multi-layer multiscale and culture-aware ethics perspective, showcasing MI use cases in 54 African countries through 400 articles on MI research, industry, government actions, as well as uses in art, music, the informal economy, and small businesses in Africa. The survey also opens discussions on the reliability of MI rankings and indexes in the African continent as well as algorithmic definitions of unclear terms used in MI.

Brief description: In this article, we will discuss a new and advanced technique in the field of artificial intelligence, which is machine learning using deep neural networks. We will learn about the nature of this technique and how it works, as well as the current and future uses of this innovative technology. Machine learning using deep neural networks is a new and advanced technology in the field of artificial intelligence, which relies on simulating human neural networks in mathematical operations. This technology is characterized by its ability to recognize patterns, predict outcomes, learn from mistakes, and improve performance over time. This technology is used in many modern applications such as speech and image recognition, automatic translation, text recognition, facial recognition, object and location recognition, and much more.

Machine learning (ML) and Artificial Intelligence (AI) have been receiving a lot of public interest in recent years, with both terms being practically common in the IT language. Despite their similarities, there are some important differences between ML and AI that are frequently neglected. Thus we will cover the key differences between ML and AI in this blog so that you can understand how these two technologies vary and how they may be utilized together. Machine Learning (ML) is a subfield of Artificial Intelligence (AI) that automates data analysis and prediction using algorithms and statistical models. It allows systems to recognize patterns and correlations in vast amounts of data and can be applied to a range of applications like image recognition, natural language processing, and others.

Aston University has completed a two-year Knowledge Transfer Partnership (KTP) with Coventry-based global transport technology firm Aurrigo, resulting in a sophisticated machine vision solution making its autonomous vehicles more capable. A KTP is a UK-wide program that helps businesses to improve their competitiveness and productivity through the better use of knowledge, technology and skills. This project has led to Aurrigo's driverless vehicles being able to see and recognize objects in greater detail, resulting in improved performance across a wider spectrum of operational domains. Previously the company's driverless vehicles were only capable of detecting that there was an object in their path and not the type of object. The project team leveraged computer vision systems, coupled with machine learning and artificial intelligence, to differentiate between objects of interest.

Your email has been saved. We'll be in touch soon. AivelX is a cutting-edge trading algorithm that leverages advanced statistical analysis to help traders reducing their risks by avoiding entering and exiting trades too early in stocks, forex, and cryptocurrency markets. This is achieved with a data-driven approach through our proprietary analysis techniques, which utilize machine learning and artificial intelligence to calculate the probability for the price to reach specific key levels. AivelX will be available as an exclusive Discord community for like-minded algorithmic traders.

The rise of ChatGPT has been nothing short of spectacular. Within two months of launch, the artificial intelligence (AI)-based application reached 100 million unique users. In January 2023 alone, ChatGPT registered about 590 million visits. In addition to AI, blockchain is another disruptive technology with increasing adoption. Decentralized protocols, applications and business models have matured and gained market traction since the Bitcoin (BTC) white paper was published in 2008.

If you pick up your phone and open a news app today, you're likely to come across some mention of artificial intelligence (AI). While the team at Q.ai has been working hard at using AI to manage investments for years, new developments like ChatGPT and Dall-E are captivating computer users of all backgrounds. If you don't know exactly what artificial intelligence means and how it differs from the related machine learning (ML) technology, here's a closer look at what you need to know about them when searching for profitable investments. The phrase artificial intelligence likely brings up images of sci-fi movies where space-ship-controlling computers or robot maids turn violent and try to take over the world. The reality of AI is much more boring than an army of computerized robots, but it's an exciting time for new AI technologies.

The war between Russia and Ukraine has highlighted a number of potential weaknesses from energy security and in supply chains of chip set supplies and commodities such as food, to the exploitation of IT, through cyber-attacks and cyber-espionage. The latter can be a strength too, as new technologies have been tested in the conflict by, for example, Ukraine, to oust Russian forces from their territories. These include autonomous and unmanned aerial vehicles (UAVs), the use of artificial intelligence for data gathering to enable "precise strikes and effective surveillance and reconnaissance, which can – at least in part – be attributed to the knowledge which the Ministry of Defence of Ukraine is receiving from the western allies"; and the use of satellites such as Elon Musk's Starlink by Ukraine to attack Russian positions. Key to the success in the conflict is Positioning, Navigation and Timing (PNT). "The ongoing Russia-Ukraine war has exposed the importance of resilient PNT for a nation's safety and security. It has opened up doors to discussions on the fragility of GPS signals that can cost millions of human lives, when data is available to an enemy entity", writes Nibedita Mohanta for Geospatial World in her article, 'Why PNT is vital for national security?'.

Technology advancement is the result of human labor. However, developing concepts in artificial intelligence, automation, and machine learning has done a significant amount of the job for us. Changes in customer service, workflows, and business procedures open up new possibilities, deal with outdated habits, and ultimately pave the way for a more secure and confident future. The banking and financial industry is a fantastic illustration of how businesses may adapt to contemporary concepts. This article will look at the relationship between machine learning and FinTech, its motivations in the industry, and its potential applications.