The market for AI technology is surging in 2020. A study by Tractica found that the size of the AI market was $9.5 billion in 2018 and expected to increase 1,200% by 2025. Many factors are driving the market. The reliance of AI by certain industries is one of the biggest factors. The punting industry is among the biggest driving forces for market growth. In July, Towards Data Science published a compelling article on the state of AI in the modern gaming industry.

Setting up a profile on most dating apps is simple. You input your name, upload some photos, set your location and sexual preferences and you're launched into a sea of mostly singles to chat with, meet and take things from there. During the process, you're also giving up valuable, personal information to platforms that often monetize by selling that data to third parties you've never heard of. Not to mention, data breaches abound. Grindr, OkCupid and Tinder, for example, were at the center of controversy last week when researchers accused the companies of disclosing highly personal information and breaking privacy laws.

Many people use dating apps in order to find the love of their lives, but here are some tips to keep the information you post on your profile private.

We propose a contextual bandit based model to capture the learning and social welfare goals of a web platform in the presence of myopic users. By using payments to incentivize these agents to explore different items/recommendations, we show how the platform can learn the inherent attributes of items and achieve a sublinear regret while maximizing cumulative social welfare. We also calculate theoretical bounds on the cumulative costs of incentivization to the platform. Unlike previous works in this domain, we consider contexts to be completely adversarial, and the behavior of the adversary is unknown to the platform. Our approach can improve various engagement metrics of users on e-commerce stores, recommendation engines and matching platforms.

The UAE continues to places great importance to protecting the environment and promoting a green economy, placing sustainability at the forefront of its strategic priorities. This is in line with the UAE Vision 2021, which aims to build a sustainable environment, and a diversified and sustainable competitive economy that ensures a secure future for generations to come. Under the guidance of its wise leadership, the UAE has made great progress towards sustainability, driven by significant achievements in the adoption of advanced technologies to create a new reality and to build a leading global model for sustainable development. The UAE has recognised the importance of Artificial Intelligence (AI) as the cornerstone for achieving sustainability goals, at a time when this advanced technology is expected to contribute to the growth of the country's GDP by 35% until 2031, while also reducing government expenditures by 50% annually, cutting down the number of paper transactions and saving millions of work hours annually. The aim of the UAE Strategy for Artificial Intelligence 2031 is to improve government performance, accelerate the pace of achievements, and to create innovative and productive work environments that ensure high levels of productivity.

Chinese tech giant Baidu has created a new way of teaching language to AIs, according to a report in TechnologyReview. The new method offers better results than the ones used by Google and Microsoft, beating out both companies in the General Language and Understanding Evaluation (GLUE) competition. Baidu's new model is called Enhanced Representation through kNowledge IntEgration, or ERNIE. It was named after the Sesame Street character because Google named the former champion model the Bidirectional Encoder Representations from Transformers or BERT. To take the crown from Google, ERNIE had to outperform its rival in the nine different language tests of GLUE.

Future Apple smart speakers, like HomePod, might be much more data privacy focused. Apple has reportedly paid $200 million to acquire Seattle-based artificial intelligence company Xnor.ai, The purchase is one of many for Apple, which has become adept at vacuuming up tech startups, but it also gives us a glimpse into the company's thinking when it comes to future devices. Xnor.ai's work on hyper-efficient, low-power AI that doesn't require powerful processing or a connection to the cloud (processing locally on-device instead), neatly slots into a few areas Apple is currently working on. Whilst Apple hasn't - and doesn't typically - comment on why it acquires certain companies and how they fit into its future roadmap, we can speculate on how Xnor.ai's work fits in to the master plan.

Christmas is just around the corner, which means it's time to start planning your presents. Finding the perfect gift for your loved ones can be tricky, but don't worry, TechRadar is here to help you plan ahead. There's nothing like watching the people you care about erupt into smiles as they tear off your wrapping, and are greeted with a gift they actually love. So if you want to leave a lasting impression, the latest tech gadget can do just that. Technology is evolving so quickly that if you decided on a gizmo last year, there's always something new to choose from this year.

Online Social Networks(OSNs) have established virtual platforms enabling people to express their opinions, interests and thoughts in a variety of contexts and domains, allowing legitimate users as well as spammers and other untrustworthy users to publish and spread their content. Hence, the concept of social trust has attracted the attention of information processors/data scientists and information consumers/business firms. One of the main reasons for acquiring the value of Social Big Data (SBD) is to provide frameworks and methodologies using which the credibility of OSNs users can be evaluated. These approaches should be scalable to accommodate large-scale social data. Hence, there is a need for well comprehending of social trust to improve and expand the analysis process and inferring the credibility of SBD. Given the exposed environment's settings and fewer limitations related to OSNs, the medium allows legitimate and genuine users as well as spammers and other low trustworthy users to publish and spread their content. Hence, this paper presents an approach incorporates semantic analysis and machine learning modules to measure and predict users' trustworthiness in numerous domains in different time periods. The evaluation of the conducted experiment validates the applicability of the incorporated machine learning techniques to predict highly trustworthy domain-based users.

At CES 2020 in Las Vegas, TechRepublic's Bill Detwiler spoke with CNET and CBS News' Dan Patterson about the various technologies featured at the electronics show. The following is an edited transcript of the interview. Now with CNET and CBS News, to talk a little bit about the tech we've seen this week. Dan, you cover a lot of privacy and security, and it's always an important issue, especially at CES, as we talk about technology blending into everyday objects. What have you seen here at the show that brings to light those issues of data privacy, data security? Dan Patterson: Well, what's really interesting to watch is this evolution of CES itself.