The Merriam-Webster dictionary defines artificial intelligence (AI) as "a branch of computer science dealing with the simulation of intelligent behavior in computers" or "the capability of a machine to imitate intelligent human behavior." The layman may think of AI as mere algorithms and programs; however, there is a distinct difference from the usual programs which are task-specific and written to perform repetitive tasks. Machine learning (ML) refers to a computing machine or system's ability to teach or improve itself using experience without explicit programming for each improvement, using methods of forward chaining of algorithms derived from backward chaining of algorithm deduction from data. Deep learning is a subsection within ML focussed on using artificial neural networks to address highly abstract problems;1 however, this is still a primitive form of AI. When fully developed, it will be capable of sentient and recursive or iterative self-improvement.
The next-generation of wireless networks will enable many machine learning (ML) tools and applications to efficiently analyze various types of data collected by edge devices for inference, autonomy, and decision making purposes. However, due to resource constraints, delay limitations, and privacy challenges, edge devices cannot offload their entire collected datasets to a cloud server for centrally training their ML models or inference purposes. To overcome these challenges, distributed learning and inference techniques have been proposed as a means to enable edge devices to collaboratively train ML models without raw data exchanges, thus reducing the communication overhead and latency as well as improving data privacy. However, deploying distributed learning over wireless networks faces several challenges including the uncertain wireless environment, limited wireless resources (e.g., transmit power and radio spectrum), and hardware resources. This paper provides a comprehensive study of how distributed learning can be efficiently and effectively deployed over wireless edge networks.
Model explainability is one of the most important problems in machine learning today. It's often the case that certain "black box" models such as deep neural networks are deployed to production and are running critical systems from everything in your workplace security cameras to your smartphone. It's a scary thought that not even the developers of these algorithms understand why exactly the algorithms make the decisions they do -- or even worse, how to prevent an adversary from exploiting them. While there are many challenges facing the designer of a "black box" algorithm, it's not completely hopeless. There are actually many different ways to illuminate the decisions a model makes.
Though it's rarely discussed, its proper integration determines whether it will make customers' lives better than ever before OR become deadly dangerous if applied without human centricity. A radical paradigm shift is required to ensure that the hyper-personalization of AI banking is not compromised by a lack of expertise in AI, technology or customer banking experience. According to Temenos, 77% of banking leaders strongly believe that AI will be the biggest game changer of all advanced technologies. Amid the pandemic, 88% of customers expect companies to accelerate their digital initiatives, while 68% state that COVID-19 has elevated their expectations of brands' digital capabilities, according to Salesforce. We can see that, prior to COVID-19, experimenting with AI possibilities was more like a tick-box exercise to keep up with the slogan of innovation.
Artificial intelligence (AI) are methods that are applied to transform the way humans will interact with machines and the role that machines will play in all spheres of human life. On one hand, the immense potential of these technologies to enhance and enrich human life has led to a growing exhilaration and excitement on their use, and on the other hand, fear and apprehension of a dystopian future where machines have taken over loom on the horizon. These techniques are considered to be a category in computer science, involved in the research and application of intelligent computers. Traditional methods for modeling and optimizing complex problems require huge amounts of computing resources, and computing-based solutions can often provide valuable alternatives for efficiently solving problems. Due to making nonlinear and complex relationships between dependent and independent variables, these techniques can be performed in the field of bioengineering with a high degree of accuracy.
This article provides an overview of stochastic process and fundamental mathematical concepts that are important to understand. Stochastic variable is a variable that moves in random order. Exchange rates, interest rates or stock prices are stochastic in nature. Stochastic variables can follow wiener or Itos process. I will start by explaining what stochastic process is.
The old telephones were upgraded until they became portable devices, and later they turned into the smartphones everyone uses nowadays. Computers were also created, and they offered people a series of new activities, whether it's keeping in touch through social media, playing games, or watching movies. That's how artificial intelligence, machine learning, and the Internet of Things took over people's lives and improved them through smarter technologies and devices. Smart applications truly made our lives more convenient and gave us many options. Alexa, for instance, only needs a few commands and it can set up the lighting you prefer, turn on the music you like, and so on.
This is a primer for what is AI, what is AI fairness, why fairness is important, how bias creeps up into the system, how to tackle algorithmic bias, and the profit tradeoff. This is a broad and complex topic. Note on terms: Algorithms, artificial intelligence, automated decision making systems (ADMs), machine learning, and models are used interchangeably in this paper. Artificial intelligence means different things to different parties. The diagram below helps delineate the differences.
Ever wonder how you can create non-parametric supervised learning models with unlimited expressive power? Look no further than Gaussian Process Regression (GPR), an algorithm that learns to make predictions almost entirely from the data itself (with a little help from hyperparameters). Combining this algorithm with recent advances in computing, such as automatic differentiation, allows for applying GPRs to solve a variety of supervised machine learning problems in near-real-time. In this article, we'll discuss: This is the second article in my GPR series. For a rigorous, Ab initio introduction to Gaussian Process Regression, please check out my previous article here. Before we dive into how we can implement and use GPR, let's quickly review the mechanics and theory behind this supervised machine learning algorithm.