Lesbians on dating and hookup apps aren't looking for men, but that's what platforms like Bumble and Tinder are serving them. On today's show, Madison and Rachelle speak to some queer women who've had this problem and what sorts of issues it creates. Then they discuss the women-focused apps that've tried to fill that space, and why it's so difficult to find safe queer dates online. This podcast is produced by Daniel Schroeder, Madison Malone Kircher, and Rachelle Hampton.

Machine learning is the key technology to the future of business. AI-driven software has already helped companies improve efficiency, customer relations, and increase sales. Researchers estimate that machine learning has the potential to add $2.6 trillion in value to the marketing and sales industry by 2020, as well as another $2 trillion to manufacturing and logistics fields. According to the International Data Corporation, machine learning spending will amount to $77.6 Billion by 2022. Companies of all sizes collaborate with Python development outsourcing companies to source data scientists and create custom data analytics software.

Recently, one critical issue looms large in the field of recommender systems -- there are no effective benchmarks for rigorous evaluation -- which consequently leads to unreproducible evaluation and unfair comparison. We, therefore, conduct studies from the perspectives of practical theory and experiments, aiming at benchmarking recommendation for rigorous evaluation. Regarding the theoretical study, a series of hyper-factors affecting recommendation performance throughout the whole evaluation chain are systematically summarized and analyzed via an exhaustive review on 141 papers published at eight top-tier conferences within 2017-2020. We then classify them into model-independent and model-dependent hyper-factors, and different modes of rigorous evaluation are defined and discussed in-depth accordingly. For the experimental study, we release DaisyRec 2.0 library by integrating these hyper-factors to perform rigorous evaluation, whereby a holistic empirical study is conducted to unveil the impacts of different hyper-factors on recommendation performance. Supported by the theoretical and experimental studies, we finally create benchmarks for rigorous evaluation by proposing standardized procedures and providing performance of ten state-of-the-arts across six evaluation metrics on six datasets as a reference for later study. Overall, our work sheds light on the issues in recommendation evaluation, provides potential solutions for rigorous evaluation, and lays foundation for further investigation.

Old-fashioned love letters may be the answer, says one Japanese city whose unusual matchmaking scheme has been a surprising success. Singles in southern Japan's Miyazaki are being encouraged to put pen to paper in a low-tech search for their soulmate, part of municipal efforts to boost the low birth rate. The charm of handwritten correspondence has attracted so many young residents that organisers have decided to expand the programme to people living farther afield. Compared to online dating, "it takes longer, and inspires you to imagine the person you're in communication with," said Rie Miyata, head of a local consulting firm commissioned to run the scheme. "It's less about how good your penmanship is," she told AFP, "and more the fact that you write every single character sincerely and with care, thinking deeply about the person you're writing to." "That's what makes letters so powerful," she said.

Conversational recommender systems (CRS) aim to proactively elicit user preference and recommend high-quality items through natural language conversations. Typically, a CRS consists of a recommendation module to predict preferred items for users and a conversation module to generate appropriate responses. To develop an effective CRS, it is essential to seamlessly integrate the two modules. Existing works either design semantic alignment strategies, or share knowledge resources and representations between the two modules. However, these approaches still rely on different architectures or techniques to develop the two modules, making it difficult for effective module integration. To address this problem, we propose a unified CRS model named UniCRS based on knowledge-enhanced prompt learning. Our approach unifies the recommendation and conversation subtasks into the prompt learning paradigm, and utilizes knowledge-enhanced prompts based on a fixed pre-trained language model (PLM) to fulfill both subtasks in a unified approach. In the prompt design, we include fused knowledge representations, task-specific soft tokens, and the dialogue context, which can provide sufficient contextual information to adapt the PLM for the CRS task. Besides, for the recommendation subtask, we also incorporate the generated response template as an important part of the prompt, to enhance the information interaction between the two subtasks. Extensive experiments on two public CRS datasets have demonstrated the effectiveness of our approach.

Artificial Narrow Intelligence (ANI), or narrow intelligence, is the courteous name for the weak AI. Narrow artificial intelligence is a type of artificial intelligence in which a learning algorithm is created to perform a single function. Any knowledge acquired through this activity will not be applied to other activities. Artificial narrow intelligence is designed to complete a single activity without human help successfully. Language translation and image recognition are two examples of common uses for narrow AI.

Advances in machine learning, computer vision, or autonomous processes will open a vast array of opportunities to organizations and employees. This will force us to rethink many aspects of our life and work. For example, virtual personal assistants can understand text, context and tone of voice, converse in natural language, make human-like gestures and even support decision making. The algorithms provide supervised and unsupervised learning capabilities, can be programmed in virtually any language and can be deployed at scale in any location. Using historical data, they create unique AI models that are perfectly fitting specific business and life environments.

AI is all around us, whether we're aware or not. Some of our interactions with AI are obvious. Digital assistants like Siri or Alexa have analyzed billions of voices to continually improve their understanding of language. But other AI is developing in the background. Weather forecast models constantly compare their forecasts to the real weather to get better.

Application of Artificial Intelligence, AI has become one of the hottest buzzwords in tech and with good reason. It is becoming essential for today's time because it can solve complex problems. Applications of AI include Gaming, Speech Recognition, Vision Systems, Healthcare, Automotive etc . Alphago: is the very first AI program that was able to beat a professional players, 2-dan player Fan Hul in October 2015, on a full sized board with no handicaps. Google Assistant: It is designed to have conversations with you in order to complete tasks. It uses AI to process natural language and perform task book appointments and make a calls.

Successful business entrepreneurs of today gather and analyze vast volumes of data in order to get as much economic boost as possible. So, how can they gain the above purpose these days in the most productive manner possible? There's a hint: using artificial intelligence (AI), machine learning (ML), and data science (DS) to acquire and analyze company data may benefit businesses. Whereas the names for the said cutting-edge innovations are occasionally applied interchangeably, it's incorrect to mix them together. The present text will tell the reasons, emphasizing their distinguishing characteristics and provide further information about their operation as well.