From customer support, business intelligence, service management, lead generation to information retrieval, chatbots have gained widespread adoption across functions. The reason why organizations are actively embracing bot technology is that chatbots not only have several high value business use cases but also are easy to deploy with minimum risks. If you believe that your organization can greatly benefit from investing in chatbots but your top management is still on the fence about it, we are here to help. Here are some ways you can strengthen your business case and persuade your leadership/executive sponsors to deploy enterprise chatbots. Data and facts help you sharpen your pitch and make decision-making simpler for your stakeholders.

The Regent Honeyeaters of Australasia are forgetting how to talk. The songbird's habitat has been so severely devastated that its numbers are dwindling. Worse, the ones that remain are so scattered that the adult males are too far apart to teach the young how to sing for a mate -- how to speak their own language. The gradual loss of the Honeyeaters' song, their primary tool for wooing a partner, creates a vicious circle of spiraling decline. Humans, on the other hand, cannot shut up.

Here's what you need to know as you start your day State Department was steered away from coronavirus origins probe, ex-officials say State Department leaders were warned not to pursue an investigation into the origins of the coronavirus, former department officials told Fox News on Thursday. The concern was that a probe would bring attention to U.S. funding of research at the Wuhan institute from which the virus may have escaped. Vanity Fair reported that officials calling for transparency from the Chinese government were told not to explore the Wuhan Institute of Virology's "gain of function" research, because it would bring what the outlet described as "unwelcome" attention of U.S. government funding into that research. The outlet reported that Thomas DiNanno, a former acting assistant secretary of the State Department's Bureau of Arms Control, Verification, and Compliance, wrote in a January memo that staff from two bureaus "warned" leaders within his office not to probe the origins of the virus because it risked opening "a can of worms." Multiple former State Department officials told Fox News that the reported memo accurately describes what was happening at State at the time and that there was an effort among some officials at the department to oppose an extensive investigation into a possible lab leak.

Technology companies have produced varied responses to concerns about the effects of the design of their conversational AI systems. Some have claimed that their voice assistants are in fact not gendered or human-like -- despite design features suggesting the contrary. We compare these claims to user perceptions by analysing the pronouns they use when referring to AI assistants. We also examine systems' responses and the extent to which they generate output which is gendered and anthropomorphic. We find that, while some companies appear to be addressing the ethical concerns raised, in some cases, their claims do not seem to hold true. In particular, our results show that system outputs are ambiguous as to the humanness of the systems, and that users tend to personify and gender them as a result.

Despite recent successes of large pre-trained language models in solving reasoning tasks, their inference capabilities remain opaque. We posit that such models can be made more interpretable by explicitly generating interim inference rules, and using them to guide the generation of task-specific textual outputs. In this paper we present COINS, a recursive inference framework that i) iteratively reads context sentences, ii) dynamically generates contextualized inference rules, encodes them, and iii) uses them to guide task-specific output generation. We apply COINS to a Narrative Story Completion task that asks a model to complete a story with missing sentences, to produce a coherent story with plausible logical connections, causal relationships, and temporal dependencies. By modularizing inference and sentence generation steps in a recurrent model, we aim to make reasoning steps and their effects on next sentence generation transparent. Our automatic and manual evaluations show that the model generates better story sentences than SOTA baselines, especially in terms of coherence. We further demonstrate improved performance over strong pre-trained LMs in generating commonsense inference rules. The recursive nature of COINS holds the potential for controlled generation of longer sequences.

Gromov-Wasserstein (GW) distance is a key tool for manifold learning and cross-domain learning, allowing the comparison of distributions that do not live in the same metric space. Because of its high computational complexity, several approximate GW distances have been proposed based on entropy regularization or on slicing, and one-dimensional GW computation. In this paper, we propose a novel approach for comparing two incomparable distributions, that hinges on the idea of distributional slicing, embeddings, and on computing the closed-form Wasserstein distance between the sliced distributions. We provide a theoretical analysis of this new divergence, called distributional sliced embedding (DSE) discrepancy, and we show that it preserves several interesting properties of GW distance including rotation-invariance. We show that the embeddings involved in DSE can be efficiently learned. Finally, we provide a large set of experiments illustrating the behavior of DSE as a divergence in the context of generative modeling and in query framework.

Well, unlike many news organisations, we have no sponsors, no corporate or ideological interests. We don't put up a paywall – we believe in free access to information of public interest. Media ownership in Australia is one of the most concentrated in the world (Learn more). Since the trend of consolidation is and has historically been upward, fewer and fewer individuals or organizations control increasing shares of the mass media in our country. According to independent assessment, about 98% of the media sector is held by three conglomerates.

Could be a price war coming to the wireless ear bud battle? Less than a month after Amazon's Echo Buds debuted undercutting Apple's AirPods, Google is launching its newest Pixel Buds at a lower price than Amazon's latest – and lower than previous Pixel Buds ($179), released last year. Pixel Buds A-Series ($99) can be pre-ordered today on Google's web site and will be shipped by June 17. Google's latest may not have all the bells and whistles found on pricier pods – such as the noise cancellation you get on AirPods Pro ($249) and Samsung's Galaxy Buds Pro ($199). But the new Pixel Buds may be just the right choice if you are looking to join the wireless wave.

As artificial intelligence (AI) systems get increasingly complex, they are being used to make forecasts – or rather generate predictive model results – in more and more areas of our lives. But at the same time, concerns are on the rise about reliability, amid widening margins of error in elaborate AI predictions. How can we address these concerns? Management science offers a set of tools that can make AI systems more trustworthy, according to Thomas G Dietterich, professor emeritus and director of intelligent systems research at Oregon State University. During a webinar on the AI for Good platform hosted by the International Telecommunication Union (ITU), Dietterich told the audience that the discipline that brings human decision-makers to the top of their game can also be applied to machines.

The Angry Birds AI competition has been held over many years to encourage the development of AI agents that can play Angry Birds game levels better than human players. Many different agents with various approaches have been employed over the competition's lifetime to solve this task. Even though the performance of these agents has increased significantly over the past few years, they still show major drawbacks in playing deceptive levels. This is because most of the current agents try to identify the best next shot rather than planning an effective sequence of shots. In order to encourage advancements in such agents, we present an automated methodology to generate deceptive game levels for Angry Birds. Even though there are many existing content generators for Angry Birds, they do not focus on generating deceptive levels. In this paper, we propose a procedure to generate deceptive levels for six deception categories that can fool the state-of-the-art Angry Birds playing AI agents. Our results show that generated deceptive levels exhibit similar characteristics of human-created deceptive levels. Additionally, we define metrics to measure the stability, solvability, and degree of deception of the generated levels.