Its impact can be far more profound and felt across the entire organisation, affecting the ways team interact and the way leaders lead. If AI is to deliver on its true potential for your organisation, it needs cultural as well as technical change. In this Breakfast Briefing, Part of BIMA's Age of AI series in partnership with Microsoft, an expert panel will help you explore the building blocks of an AI culture, including: So how do you create quality data available to all? Empowerment: Many of the most profound impacts of AI come from people closest to the business. So how do you encourage greater collaboration?

Connect, download a free E-Book, watch a keynote, or browse my blog. What Artificial Intelligence (AI) helps take away with one hand, namely privacy, even if you're wearing masks, and with the Chinese relatively dystopian feeling Social Credit Scoring (SCS) system being a prime example, it gives with the other. And as for what it gives back, ironically, that's also privacy. Well, just one of the many ways that companies strip away our privacy is by using facial recognition, for example, from images and video, which they then use to track us, monitor us, and profile us all. Now, however, the same AI technology that's behind DeepFakes could soon be used to help anonymize and hide us online and confuse these facial recognition systems.

Geographically, the global artificial intelligence in retail market is segmented into five major regions, namely, North America, Europe, Asia Pacific, Latin America, and the Middle East and Africa. The global AI in retail market is analyzed methodically with respect to major countries in each of the regions with the help of bottom-up approach to arrive at the most precise market estimation. At present, North America holds a dominating position in the global AI in retail market. The region has high technology adoption rate, presence of key players & start-ups, and high penetration of internet. Consequently, North America is expected to retain its dominance throughout the forecast period.

Virgin Media says its internet service is working as normal, despite reports of an outage. Numerous articles and consumer website uSwitch said that Virgin's broadband was being hit by issues and users were unable to get online. But the company says there is no major outage and that most people should still be able to connect as usual. "Our broadband services are running as normal – there is no national broadband outage affecting our network," a Virgin Media spokesperson said. Confusion around the outage was increased after Virgin Media customers were directed to its service status page.

The CamemBERT Transformer model (by Facebook AI, Inria and Sorbonne Université), trained on 138GB of French text was added this morning to the huggingface/transformers model repository, and is now usable in both PyTorch and TensorFlow 2! Install the library from source to play around with it! It is available alongside chinese and german BERT models and other multi-lingual models. CamemBERT improves the state of the art on several French NLP tasks, outperforming multi-lingual models in several tasks. It's based on RoBERTa's training scheme but uses whole-word masking as well as sentence-piece tokenization.

We study the problem of finding fake online news. This is an important problem as news of questionable credibility have recently been proliferating in social media at an alarming scale. As this is an understudied problem, especially for languages other than English, we first collect and release to the research community three new balanced credible vs. fake news datasets derived from four online sources. We then propose a language-independent approach for automatically distinguishing credible from fake news, based on a rich feature set. In particular, we use linguistic ( n-gram), credibility-related (capitalization, punctuation, pronoun use, sentiment polarity), and semantic (embeddings and DB-Pedia data) features. Our experiments on three different testsets show that our model can distinguish credible from fake news with very high accuracy.

--Wide spread of satirical news in online communities is an ongoing trend. The nature of satires are so inherently ambiguous that sometimes it's too hard even for humans to understand whether it's actually satire or not. So, research interest has grown in this field. The purpose of this research is to detect Bangla satirical news spread in online news portals as well as social media. In this paper we propose a hybrid technique for extracting feature from text documents combining Word2V ecand TF-IDF. Using our proposed feature extraction technique, with standard CNN architecture we could detect whether a Bangla text document is satire or not with an accuracy of more than 96%. Satires can be considered as a literary form which involves a delicate balance between criticism and humor.

We initiate the study of multi-stage episodic reinforcement learning under adversarial manipulations in both the rewards and the transition probabilities of the underlying system. Existing efficient algorithms heavily rely on the "optimism under uncertainty" principle which dictates their behavior and does not allow flexibility to perform corruption-robust exploration. We address this by (i) departing from the optimistic behavior, and (ii) creating a general framework that incorporates the principle of action-elimination. (This principle has been essential for corruption-robust exploration in multi-armed bandits, a degenerate special case of episodic reinforcement learning.) Despite constructing a lower bound for a straightforward implementation of action-elimination, we provide a clean and modular way to transfer it to episodic reinforcement learning. Our algorithm enjoys near-optimal guarantees in the absence of adversarial manipulations, has performance that degrades gracefully as the amount of corruption increases, and does not need to know this amount. Our results shed new light on the broader question of robust exploration, and suggest a way to address a rather daunting mismatch between optimistic algorithms and algorithms with higher flexibility. To demonstrate the applicability of our framework, we provide a second instantiation thereof, showing how it can provide efficient guarantees for the stochastic setting, despite doing almost uniform exploration across plausibly optimal actions.

Certain research strands can yield "forbidden knowledge". This term refers to knowledge that is considered too sensitive, dangerous or taboo to be produced or shared. Discourses about such publication restrictions are already entrenched in scientific fields like IT security, synthetic biology or nuclear physics research. This paper makes the case for transferring this discourse to machine learning research. Some machine learning applications can very easily be misused and unfold harmful consequences, for instance with regard to generative video or text synthesis, personality analysis, behavior manipulation, software vulnerability detection and the like. Up to now, the machine learning research community embraces the idea of open access. However, this is opposed to precautionary efforts to prevent the malicious use of machine learning applications. Information about or from such applications may, if improperly disclosed, cause harm to people, organizations or whole societies. Hence, the goal of this work is to outline norms that can help to decide whether and when the dissemination of such information should be prevented. It proposes review parameters for the machine learning community to establish an ethical framework on how to deal with forbidden knowledge and dual-use applications.

Apple (NasdaqGS:AAPL) and Salesforce (NYSE:CRM) have announced the launch of two new apps, including the AI-powered redesigned Salesforce Mobile App with features exclusive to iOS and iPadOS. "With Salesforce Mobile, Salesforce and Apple are empowering sales, service and marketing professionals on the go to deliver game-changing customer experiences, powered by AI. And with Trailhead GO, millions more can now skill up for free, anytime and anywhere, to learn in-demand skills and fill the jobs of today and tomorrow." Intel Corporation (NasdaqGS:INTC) has unveiled oneAPI, "a unified and scalable programming model to harness the power of diverse computing architectures in the era of HPC/AI convergence", and "a general-purpose GPU optimized for HPC/AI acceleration based on the Xe architecture". "HPC and AI workloads demand diverse architectures, ranging from CPUs, general-purpose GPUs and FPGAs, to more specialized deep-learning NNPs, which Intel demonstrated earlier this month. Simplifying our customers' ability to harness the power of diverse computing environments is paramount, and Intel is committed to taking a software-first approach that delivers a unified and scalable abstraction for heterogeneous architectures."