Today, artificial intelligence (AI) has become an irreplaceable part of how we shop and do business on the web. It's a key component of the underlying infrastructure that brands and retailers rely on to engage customers, track trends, make better business decisions, and provide the most optimal, personalized customer experiences possible. Here are the top nine trends in eCommerce AI that you can expect to see in 2022 and 2023. AI voice assistants like Amazon's Alexa, Apple's Siri, and Google Assistant have become household names used by millions of people worldwide. In fact, 27% of shoppers took advantage of voice assistants to make online purchases in 2020, accounting for $40 billion of revenue in the U.S. and the UK alone.

At the I/O 2022 conference this past May, Google CEO Sundar Pichai announced that the company would, in the coming months, gradually avail its experimental LaMDA 2 conversational AI model to select beta users. On Thursday, researchers at Google's AI division announced that interested users can register to explore the model as access increasingly becomes available. Regular readers will recognize LaMDA as the supposedly sentient natural language processing (NLP) model that a Google researcher got himself fired over. NLPs are a class of AI model designed to parse human speech into actionable commands and are behind the functionality of digital assistants and chatbots like Siri or Alexa, as well as do the heavy lifting for realtime translation and subtitle apps. Basically, whenever you're talking to a computer, it's using NLP tech to listen.

A phone number is a personal identifier that people use to contact someone. While it may seem okay to hand out your number to everyone you meet, it's not always the best idea. If you're not careful, you could inadvertently give out your number to a scammer or someone with malicious intent. And that's where a secret phone number comes in handy. These numbers can be used for a variety of purposes, such as temporary business numbers, disposable numbers for online dating, or one-time use when signing up for new services.

Are you wondering how to text on tinder? If you're interested in the latest and hottest method for meeting women, then you'll want to keep reading. The rise of this dating app has been amazing. It's so much easier now, since there are so many more options. That said, learning how to text on tinder is critical if you want to make a good impression with women.

What is personalized marketing, how does it boost your conversion rate, and how can machine learning help you tailor content to a wider audience?

Bundle recommendation aims to recommend the user a bundle of items as a whole. Nevertheless, they usually neglect the diversity of the user's intents on adopting items and fail to disentangle the user's intents in representations. In the real scenario of bundle recommendation, a user's intent may be naturally distributed in the different bundles of that user (Global view), while a bundle may contain multiple intents of a user (Local view). Each view has its advantages for intent disentangling: 1) From the global view, more items are involved to present each intent, which can demonstrate the user's preference under each intent more clearly. 2) From the local view, it can reveal the association among items under each intent since items within the same bundle are highly correlated to each other. To this end, we propose a novel model named Multi-view Intent Disentangle Graph Networks (MIDGN), which is capable of precisely and comprehensively capturing the diversity of the user's intent and items' associations at the finer granularity. Specifically, MIDGN disentangles the user's intents from two different perspectives, respectively: 1) In the global level, MIDGN disentangles the user's intent coupled with inter-bundle items; 2) In the Local level, MIDGN disentangles the user's intent coupled with items within each bundle. Meanwhile, we compare the user's intents disentangled from different views under the contrast learning framework to improve the learned intents. Extensive experiments conducted on two benchmark datasets demonstrate that MIDGN outperforms the state-of-the-art methods by over 10.7% and 26.8%, respectively.

Multi-scenario recommendation is dedicated to retrieve relevant items for users in multiple scenarios, which is ubiquitous in industrial recommendation systems. These scenarios enjoy portions of overlaps in users and items, while the distribution of different scenarios is different. The key point of multi-scenario modeling is to efficiently maximize the use of whole-scenario information and granularly generate adaptive representations both for users and items among multiple scenarios. we summarize three practical challenges which are not well solved for multi-scenario modeling: (1) Lacking of fine-grained and decoupled information transfer controls among multiple scenarios. (2) Insufficient exploitation of entire space samples. (3) Item's multi-scenario representation disentanglement problem. In this paper, we propose a Scenario-Adaptive and Self-Supervised (SASS) model to solve the three challenges mentioned above. Specifically, we design a Multi-Layer Scenario Adaptive Transfer (ML-SAT) module with scenario-adaptive gate units to select and fuse effective transfer information from whole scenario to individual scenario in a quite fine-grained and decoupled way. To sufficiently exploit the power of entire space samples, a two-stage training process including pre-training and fine-tune is introduced. The pre-training stage is based on a scenario-supervised contrastive learning task with the training samples drawn from labeled and unlabeled data spaces. The model is created symmetrically both in user side and item side, so that we can get distinguishing representations of items in different scenarios. Extensive experimental results on public and industrial datasets demonstrate the superiority of the SASS model over state-of-the-art methods. This model also achieves more than 8.0% improvement on Average Watching Time Per User in online A/B tests.

Human intelligence is able to first learn some basic skills for solving basic problems and then assemble such basic skills into complex skills for solving complex or new problems. For example, the basic skills "dig hole," "put tree," "backfill" and "watering" compose a complex skill "plant a tree". Besides, some basic skills can be reused for solving other problems. For example, the basic skill "dig hole" not only can be used for planting a tree, but also can be used for mining treasures, building a drain, or landfilling. The ability to learn basic skills and reuse them for various tasks is very important for humans because it helps to avoid learning too many skills for solving each individual task, and makes it possible to solve a compositional number of tasks by learning just a few number of basic skills, which saves a considerable amount of memory and computation in the human brain. We believe that machine intelligence should also capture the ability of learning basic skills and reusing them by composing into complex skills. In computer science language, each basic skill is a "module", which is a reusable network of a concrete meaning and performs a specific basic operation. The modules are assembled into a bigger "model" for doing a more complex task. The assembling procedure is adaptive to the input or task, i.e., for a given task, the modules should be assembled into the best model for solving the task. As a result, different inputs or tasks could have different assembled models, which enables Auto-Assembling AI (AAAI). In this work, we propose Modularized Adaptive Neural Architecture Search (MANAS) to demonstrate the above idea. Experiments on different datasets show that the adaptive architecture assembled by MANAS outperforms static global architectures. Further experiments and empirical analysis provide insights to the effectiveness of MANAS.

Like Gatsby's endless examination of Daisy's green light across the bay, the singleton's incessant search for "ideal" remains always out of reach (I'm a dating app evangelist – but even I'm not on Tinder any more, 15 August; Dating apps have made our love lives hell. Why do we keep using them?, 16 August). The promise exceeds what reality will deliver: the facade of beauty, wit and chemistry conjured through our screens belies doctored images, unrepentant creeps and bores. The collective romantic subconscious, carefully curated by Disney and Richard Curtis, cannot survive its collision with reality. And this is to say nothing of those we leave in our wake: the flattened Myrtle Wilsons and proverbial pulped fruits at Gatsby's door waiting for the next swipe-right to pick through.

Artificial intelligence is defined as the intelligence displayed by machines, while natural intelligence is the term coined for the intelligence demonstrated by humans. Over the years, AI has developed multiple applications such as search engines, recommendation systems, and self-driving cars, along with the capability of machines to understand human speech in personal assistants such as Siri and Cortana. As an academic discipline, artificial intelligence was founded in the 1950s after Alan Turing's "I propose to consider the question'can machines think'?" in the academic journal "Mind". However, major advancements came decades later. According to Allied Market Research, the global artificial intelligence market was worth $65.48 billion in 2020 and is expected to grow to $1.58 trillion by 2030 at a CAGR of 38%.