In recent years, voice-based virtual assistants such as Google Assistant and Amazon Alexa have grown popular. This has presented both potential and challenges for natural language understanding (NLU) systems. These devices' production systems are often trained by supervised learning and rely significantly on annotated data. But, data annotation is costly and time-consuming. Furthermore, model updates using offline supervised learning can take long and miss trending requests.

Conversational recommender systems (CRS) aim to recommend suitable items to users through natural language conversations. For developing effective CRSs, a major technical issue is how to accurately infer user preference from very limited conversation context. To address issue, a promising solution is to incorporate external data for enriching the context information. However, prior studies mainly focus on designing fusion models tailored for some specific type of external data, which is not general to model and utilize multi-type external data. To effectively leverage multi-type external data, we propose a novel coarse-to-fine contrastive learning framework to improve data semantic fusion for CRS. In our approach, we first extract and represent multi-grained semantic units from different data signals, and then align the associated multi-type semantic units in a coarse-to-fine way. To implement this framework, we design both coarse-grained and fine-grained procedures for modeling user preference, where the former focuses on more general, coarse-grained semantic fusion and the latter focuses on more specific, fine-grained semantic fusion. Such an approach can be extended to incorporate more kinds of external data. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach in both recommendation and conversation tasks.

Common AI uses include intelligent assistants such as Siri, Alexa, or Google Assistant, but one Huntsville contractor is looking into its uses on the battlefield.

Recent natural language processing (NLP) techniques have accomplished high performance on benchmark datasets, primarily due to the significant improvement in the performance of deep learning. The advances in the research community have led to great enhancements in state-of-the-art production systems for NLP tasks, such as virtual assistants, speech recognition, and sentiment analysis. However, such NLP systems still often fail when tested with adversarial attacks. The initial lack of robustness exposed troubling gaps in current models' language understanding capabilities, creating problems when NLP systems are deployed in real life. In this paper, we present a structured overview of NLP robustness research by summarizing the literature in a systemic way across various dimensions. We then take a deep-dive into the various dimensions of robustness, across techniques, metrics, embeddings, and benchmarks. Finally, we argue that robustness should be multi-dimensional, provide insights into current research, identify gaps in the literature to suggest directions worth pursuing to address these gaps.

Let's answer the basic question "who's better, humans or robots" Well… It's not that simple, as we know robots are the machines created by humans to do specific kinds of stuff or the tasks assigned to them, we have seen robots working in factories and recently as assistants in hospitals, and waiters to serve food. On the other hand, humans are able to do anything they put their mind into, they have the power of decisions, intellect, to discover and to learn. Now the real competition here is between the human brain and computer's brain (CPU). Human brain, and the nervous system is our trump card. You May Also Like: 15 Things You Probably Didn't Know About Brain This is where AI comes in, Artificial Intelligence gives robots the ability to make decisions on the basis of algorithms provided in the code, which is basically machine learning.

Can't figure out how to use your new tech toy? While you may have found a new phone, smart speaker, tablet or laptop under the tree this holiday season, you might be a little overwhelmed with all its features. In fact, whether you're tech-savvy or tech-shy, many of us stick to what we know and repeat those actions over and over, opposed to venturing a little outside our comfort zone. That's ok, of course, but should you want to learn a few tech tips and tricks – to help save you time, money and stress – we've got a half-dozen of ideas here for you, covering a wide range of popular products. Typing on your iPhone and want to undo what you just wrote?

If you've been a fan of Marvel Comics, you know Jarvis, who is a personal assistant of Iron Man. He knows everything and he can control everything related to computer tasks. In this tutorial, I am going to create a similar kind of program using the Python programming language.

Looking back at 2021 it was a different kind of year for voice AI than in the past. We could have said that in 2020 as well, but that was true on every societal and economic level due to the pandemic. However, we saw the seeds of change in the voice AI industry in late 2019 that was paused briefly and then accelerated in 2020 which set up 2021 to be the year of enterprise adoption. Voice AI news, innovation, and investment were largely driven by consumer applications in the period 2016 – 2019. Amazon, Google, and Apple (and occasionally Samsung) dominated the headlines related to the technology.

By 2022, the global ML market is expected to be worth $8.81 billion. It's not a surprise that Artificial Intelligence (AI) and Machine Learning (ML) are two of the top buzzwords in today's technological world. But, how will the two technologies create innovation and change in the near future? Do you have the answer? If not, continue reading to learn why AI and ML are two of the most promising technologies that will drive innovation and change in the coming years.

The evolving banking landscape has pushed traditional banks to digitalize in order to cater to consumer demand for personalized and value-added services. This is especially prominent given the effects of the Covid-19 pandemic, which has driven a majority of the customer base towards digital services. Whilst many emerging technologies exist, there have been variations in adoption rates by traditional banks around the world, with trends showing that they have been generally slower to adopt these technologies. Typically, traditional banks have been rolling out their digitalization efforts conservatively, usually with a multi-channel approach. These include improving existing digital channels or ramping up efforts to launch independent digital banking businesses.