The wireless industry has been one of the fastest growing spaces for several decades. That does not mean, however, that it is always on fire. Every growth wave has ebbs and flows. It all depends on the period of time in which you are focused. The good news is the wireless industry has entered the next growth wave with 5G, AI, IoT, AR, VR, cloud and more.

Commercial machine learning (ML) applications have progressed from conceptualization to testing to deployment over the past decade. The need for efficient and scalable operations has led to the establishment of MLOps as a vital function within firms developing artificial intelligence (AI) as the industry has progressed through this cycle. As a result, it is critical to understand what ML DataOps is and how it affects various sectors. ML relies heavily on the collection, analysis, and creation of data. Over the past year, the AI ecosystem has witnessed a push to move to a more data-centric approach from the current model-centric one.

This article is part of our series that explores the business of artificial intelligence. Digital technologies, and at their forefront artificial intelligence, are triggering fundamental shifts in society, politics, education, economy, and other fundamental aspects of life. These changes provide opportunities for unprecedented growth across different sectors of the economy. But at the same time, they entail challenges that organizations must overcome before they can tap into their full potential. In a recent talk at an online conference organized by Stanford Human-Centered Artificial Intelligence (HAI), Stanford professor Erik Brynjolfsson discussed some of these opportunities and challenges.

Digital technologies, and at their forefront artificial intelligence, are triggering fundamental shifts in society, politics, education, economy, and other fundamental aspects of life. These changes provide opportunities for unprecedented growth across different sectors of the economy. But at the same time, they entail challenges that organizations must overcome before they can tap into their full potential. In a recent talk at an online conference organized by Stanford Human-Centered Artificial Intelligence (HAI), Stanford professor Erik Brynjolfsson discussed some of these opportunities and challenges. Brynjolfsson, who directs Stanford's Digital Economy Lab, believes that in the coming decade, the use of artificial intelligence will be much more widespread than it is today.

Natural language processing, frequently known as NLP, alludes to the ability of a computer to comprehend human speech as it is spoken. NLP is a key segment of artificial intelligence (AI) and depends on machine learning, a particular type of AI that analyzes and utilizes patterns in information to improve a program's comprehension of speech. Analytics Insight has forecasted the Market Revenue of NLP to at US$8,319 million, with a CAGR of 18.10% between 2019 and 2024. Natural language processing (NLP) can be effectively used in education for promoting language learning and improving the academic performance of the students. It assists in developing an effective process of learning in the educational setting by developing scientific approaches which can process of using computer and internet for enhancing the learning.

Over the last couple of decades in the lending industry, financial disintermediation has occurred on a global scale. Traditionally, even for small supply of funds, banks would act as the conduit between the funds and the borrowers. It has now been possible to overcome some of the obstacles associated with such supply of funds with the advent of online platforms like Kiva, Prosper, LendingClub. Kiva for example, works with Micro Finance Institutions (MFIs) in developing countries to build Internet profiles of borrowers with a brief biography, loan requested, loan term, and purpose. Kiva, in particular, allows lenders to fund projects in different sectors through group or individual funding. Traditional research studies have investigated various factors behind lender preferences purely from the perspective of loan attributes and only until recently have some cross-country cultural preferences been investigated. In this paper, we investigate lender perceptions of economic factors of the borrower countries in relation to their preferences towards loans associated with different sectors. We find that the influence from economic factors and loan attributes can have substantially different roles to play for different sectors in achieving faster funding. We formally investigate and quantify the hidden biases prevalent in different loan sectors using recent tools from causal inference and regression models that rely on Bayesian variable selection methods. We then extend these models to incorporate fairness constraints based on our empirical analysis and find that such models can still achieve near comparable results with respect to baseline regression models.

There has been a slew of newer sector-specific strategy documents in India that have discussed the role of artificial intelligence as a disruptive technological force in the future. But these strategy documents mentioning AI need to do more to highlight its implications for access to services in a particular sector. Researchers need to bring specificity in not just impact, but also applications and ethics into their analysis. As per the report of the'Steering Committee on Fintech Related Issues' released in September, "AI is expected to transform the manner in delivery of such (financial) services". The roadmap for'Health System for a New India' released by Niti Aayog on 18 November mentions the need to keep up with global technological changes such as AI to "augment clinicians' knowledge".

There has been a slew of newer sector-specific strategy documents in India that have discussed the role of artificial intelligence as a disruptive technological force in the future. But these strategy documents mentioning AI need to do more to highlight its implications for access to services in a particular sector. Researchers need to bring specificity in not just impact, but also applications and ethics into their analysis. As per the report of the'Steering Committee on Fintech Related Issues' released in September, "AI is expected to transform the manner in delivery of such (financial) services". The roadmap for'Health System for a New India' released by Niti Aayog on 18 November mentions the need to keep up with global technological changes such as AI to "augment clinicians' knowledge".

In our last blog topic on data lineage "Top 6 Open Source Data Lineage Tools", we discussed on what is data lineage and importance of data lineage along with top open-source & paid data lineage tools. In this blog, we will cover the top 10 real-life data lineage examples. This blog will focus on the significance and benefits of data lineage for below mentioned companies. Standard Chartered, a British multinational bank, needs no formal introduction. The bank is one of the global leaders not only in terms of the users but also in terms of its data analytics sophistication.

Enterprises adopt artificial intelligence in an effort to positively impact their business performance. But the power of AI goes beyond business and can even change human experiences. This 21st century technology is serving as a driver and even impacting consumer services across a variety of industries, from retail, finance and beyond. The following client experiences serve as a gateway to better understand AI, which not only helps create a reaction, the technology can also help us act proactively in advance. Imagine a young couple who just became first-time parents and want the peace of mind that if anything happens, their new family member is protected.