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.

Higher-order tensor datasets are rising ubiquitously in modern data science applications, for instance, recommendation systems (Baltrunas et al., 2011; Bi et al., 2018), social networks (Bickel and Chen, 2009), genomics (Hore et al., 2016), and neuroimaging (Zhou et al., 2013). Tensor provides effective representation of data structure that classical vector-and matrix-based methods fail to capture. One example is music recommendation system (Baltrunas et al., 2011) that records ratings of songs from users on various contexts. This three-way tensor of user song context allows us to investigate interactions of users and songs in a context-specific manner. Another example is network dataset that records the connections among a set of nodes. Pairwise interactions are often insufficient to capture the complex relationships, whereas multi-way interactions improve the understanding of networks in molecular system (Young et al., 2018) and social networks (Han et al., 2020). In both examples, higher-order tensors represent multi-way interactions in an efficient way. Tensor estimation problem cannot be solved without imposing structures. An appropriate reordering of tensor entries often provides effective representation of the hidden salient structure.

Machine learning is changing almost every industry. It has revolutionized everything from agriculture to healthcare diagnosis. It has revolutionized the way businesses operate and helped to accelerate their growth. Machine learning algorithms have been adopted by the gaming industry to enhance video games' engagement. ML can be used for high-speed game development.

Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. These AI assistants in order to understand your voice they need to do speech recognition so as to understand what you have just said. Speech Recognition is a complex process, well I'm not going to teach you how to train a Machine Learning/Deep Learning Model to that, instead, I instruct you how to do that using google speech recognition API. As long as you have the basics of Python you can successfully complete this tutorial and build your own fully functioning speech recognition programs in Python.

Amazon has been sporadically discounting its second-gen Echo Buds and they're back down to $90, or $30 off their list price of $120. On Amazon Prime Day this year, they hit $80. Note that if you want a wireless charging case, the price ticks up to $110. That model normally sells for $140, so again you're looking at $30 off. I thought the Echo Buds 2 offered some welcome improvements over the originals, including better sound and noise canceling.