A race towards digitization is bringing a revolution in the Financial and FinTech sectors. At the core of this digitization lies the availability of a vast array of data (such as Big Data), advancements in affordable computing technologies, and the advent of intelligent technologies such as Machine Learning and Artificial Intelligence. AI has been around for nearly 70 years, its practicality and intelligence have increasing over time. Today, AI has become an integral part of the industrial landscape as well as the lives of common people. Examples of this can be seen in the voice assistants in smartphones, the use of AI robots in supply chain logistics, self-driving cars, movie recommendations on Netflix, and more.

Today Artificial Intelligence and Machine Learning are penetrating every aspect of business, from Chatbots being deployed to assist customers to AI-driven platforms being harnessed to automate sales processes. From powering Apple's Siri and Microsoft's Cortana to Google's Allo, AI is promising a better future. At FuGenX, we help businesses build cutting-edge AI solutions that enable them to achieve a first-mover advantage to be a leader in the better future. Our services around AI help you gain high-quality, high-accuracy AI capabilities that enables building highly scalable and cost-effective digital products and solutions. You will certainly achieve the benefit of minimized labor and infrastructure cost.

Artificial Intelligence (AI) is a wide-ranging branch of computer science focused on building smart machines capable of identifying patterns, characteristics, and relationships within data and use this information to generate a prediction – or form a judgement – and then automatically perform a task. The rapid rise in the popularity of developing and adopting artificial intelligence solutions can be attributed to several factors, particularly data inundation, the need to make faster and better data-driven decisions, and the importance of automation in order to enable businesses to scale. AI is considered one of the central pillars to digital transformation and is becoming increasingly integrated into nearly every aspect of our lives, from mobile phones to banking and even how we are marketed to as an audience. Articles on artificial intelligence are often heavy on hype and technology jargon. In this article, we keep things simple and focus on 10 of the remarkable and defining benefits for how AI can help both people and businesses.

The Oversight Board has only been up and running for less than a year, but Facebook says it's already having trouble keeping up with the group's recommendations. The company says it wants to work with the Oversight Board to "improve the recommendation process," though it's not yet clear what those changes might entail. But it suggests Facebook is looking to shake up the way it deals with the independent body it created to oversee its content policies. In a new report detailing Facebook's dealings with the Oversight Board, the company notes that it's made significant changes as the result of the Oversight Board's recommendations. These changes include updates to how it handles hate speech and nudity, as well as how it determines "newsworthy" content.

A bipartisan group of House lawmakers has introduced legislation that would give people more control over the algorithms that shape their online experience. If passed, the Filter Bubble Transparency Act would require companies like Meta to offer a version of their platforms that runs on an "input-transparent" algorithm that doesn't pull on user data to generate recommendations. The bill would not do away with "opaque" recommendation algorithms altogether but would make it a requirement to include a toggle that allows people to switch that functionality off. Additionally, platforms that continue to use recommendation algorithms need to have a notification that informs people those recommendations are based on inferences generated by their personal data. The prompt can be a one-time notice, but it would need to be presented in a "clear, conspicuous manner," according to the proposed bill. The legislation was introduced by Representatives Ken Buck (R-CO), David Cicilline (D-RI), Lori Trahan (D-MA) and Burgess Owens (R-UT).

NVIDIA has been steadily advancing its AI assistant technology in recent months, and now it's clear just how all the pieces fit together. The company has introduced Omniverse Avatar (for 3D assistant creation) and Riva (custom AI voice creation) platforms that, combined, lead to surprisingly realistic virtual personas with relatively little effort -- or, in one case, deliberately unrealistic. In one demo, used to highlight NVIDIA's AI-powered Maxine toolkit, the company created an Omniverse Avatar from a woman's photo and used Riva to train the voice based on that woman, convert text to speech and translate to different languages. The digital stand-in looks and sounds much like the real person (aside from a couple of stiff-sounding translations), and can even turn its head while maintaining natural-looking eye contact. As you might imagine, this could lead to more relatable virtual helpers at kiosks and websites.

Learning to capture feature relations effectively and efficiently is essential in clickthrough rate (CTR) prediction of modern recommendation systems. Most existing CTR prediction methods model such relations either through tedious manuallydesigned low-order interactions or through inflexible and inefficient high-order interactions, which both require extra DNN modules for implicit interaction modeling. In this paper, we proposed a novel plug-in operation, Dynamic Parameterized Operation (DPO), to learn both explicit and implicit interaction instance-wisely. We showed that the introduction of DPO into DNN modules and Attention modules can respectively benefit two main tasks in CTR prediction, enhancing the adaptiveness of feature-based modeling and improving user behavior modeling with the instance-wise locality. Our Dynamic Parameterized Networks significantly outperforms state-of-the-art methods in the offline experiments on the public dataset and real-world production dataset, together with an online A/B test. Furthermore, the proposed Dynamic Parameterized Networks has been deployed in the ranking system of one of the world's largest e-commerce companies, serving the main traffic of hundreds of millions of active users. Click-through rate (CTR) prediction, which aims to estimate the probability of a user clicking an item, is of great importance in recommendation systems and online advertising systems (Cheng et al., 2016; Guo et al., 2017; Rendle, 2010; Zhou et al., 2018b). Effective feature modeling and user behavior modeling are two critical parts of CTR prediction. Deep neural networks (DNNs) have achieved tremendous success on a variety of CTR prediction methods for feature modeling (Cheng et al., 2016; Guo et al., 2017; Wang et al., 2017). Under the hood, its core component is a linear transformation followed by a nonlinear function, which models weighted interaction between the flattened inputs and contexts by fixed kernels, regardless of the intrinsic decoupling relations from specific contexts (Rendle et al., 2020). This property makes DNN learn interaction in an implicit manner, while limiting its ability to model explicit relation, which is often captured by feature crossing component (Rendle, 2010; Song et al., 2019). Most existing solutions exploit a combinatorial framework (feature crossing component DNN component) to leverage both implicit and explicit feature interactions, which is suboptimal and inefficient (Cheng et al., 2016; Wang et al., 2017). For instance, wide & deep combines a linear module in the wide part for explicit low-order interaction and a DNN module to learn high-order feature interactions. Follow-up works such as Deep & Cross Network (DCN) follows a similar manner by replacing the wide part with more sophistic networks, however, posits restriction to input size which is inflexible.

Millennials expect everything at their fingertips-- including love. Their expectations regarding an ideal partner are evolving fast and so are social and cultural expectations. Keen to make their own choices based on the connection they share with a person, they are in no hurry to settle down or compromise until they feel comfortable with their choice of partner. "Around 67 per cent (of individuals) would rather find a meaningful relationship in the serendipity of a dating app than have friends and family arrange a set-up," says Sitara Menon, senior marketing manager of dating app OkCupid. With the proliferation of Internet, new ways and means are in place to find love.

In my blog "The Importance of Determining Intent", I discussed the importance of determining user intent to create an "intelligent" user or stakeholder experience. Analytics-centric organizations specialize in determining and codifying a user's intent in order to provide a more engaging, relevant, hyper-personalized experience (Figure 1). Figure 1: Using "Intent Determination" to Create an Intelligent Customer Experience To create an "intelligent" user experience requires leveraging AI/ML to analyze a deep history of the user's interactions to determine the user's intentions, and then coupling those intentions with current trends, patterns, and relationships to match those intentions with a deep understanding of the available content to recommend the most relevant action. We reviewed how digital marketing companies, such as those featured in Figure 1, determine user intent. These companies accumulate a deep history of each individual user's interactions including what sites or content they visited or viewed, how long they spent with each site or piece of content, what they clicked on, what they did not click on, and their contextual search requests. They analyze the user's interaction history, and match that with current trends and behaviors of similar cohorts, to determine and codify (think propensity scores) the user's intentions (areas of interest) that drives real-time recommendation decisions.

The application of Artificial Intelligence in the telecommunication industry has gained quite a much traction in the recent past and for the right reasons. The role of the telecommunications industry in today's world has expanded beyond the provision of simple phone and internet interaction services for individuals and corporates. In the current era of the Internet of Things (IoT), telecommunication companies have leveraged mobile and broadband services to take center stage in technological growth and innovation. That is not all; educated prospects point to a future commercial world where Artificial intelligence is vital. For example, Technavio, a leading market research, and advisory firm globally, expects growth in technology to continue for the foreseeable future and record a Compounded Annual Growth Rate (CAGR) of above 42% next year.