Emory University's Department of Economics hosted an event about artificial intelligence's impact on growth, jobs and inequality on Oct. 27.

Automated One-way Interview is an interviewing method for the recruiter to assess the candidate without actually spending real-time interviewing them. Real-time interviews require either the interviewer or candidate to travel and spend a significant amount of time. On the other hand, automated interviewing mode lets the recruiter send a set of questions to the candidate and allows the candidate to answer and send them back at their own comfort. There is a reason why every bit of technology is evolved and acing automated or one-way interview depends on how good a candidate understands the recruiter's motive behind it. This is the exact reason why TurboHire's one-way video interviewing took birth.

How would an AI writing program start an article on the future of AI writing? Well, there's one easy way to find out: I used the best known of these tools, OpenAI's GPT-3, to do the job for me. Using GPT-3 is disarmingly simple. You have a text box to type into and a menu on the side to adjust parameters, like the "temperature" of the response (which essentially equates to randomness). You type, hit enter, and GPT-3 completes what you've written, be it poetry, fiction, or code.

What is the future of responsible AI in the UK? There is growing recognition, as AI is increasingly present in our lives, that it must be responsibly deployed. But what does this mean in practice? And what does it mean specifically here, in the UK? AI tools offer great opportunities. Data science and machine learning techniques are increasingly deployed in industry, and might be applied, for instance, to support sustainable development initiatives, improve educational outcomes, or enhance human welfare more generally.

In the coming decade, I expect that AI will play an increasingly prominent role in the lives of people everywhere. AI-infused services will become more common, and AI will become increasingly embedded in the daily lives of people across the world. I believe that this will bring with it great economic and societal benefits, but that it will also require us to address the many challenges to ensure that the benefits are broadly shared and that people are not marginalised by these new technologies. A key insight of AI research is that it is easier to build things than to understand why they work. However, defining what success looks like for an AI application is not straightforward.

What do you think of when you hear the word automation? None of this is wrong, per se, but we like to add another word when we think about automation: productive. For us, automation is all about giving humans superhero capes. We think about it as a way for productive automated work technologies to amplify humanness and economic productivity. Or, instead of focusing on how automation replaces humans, honing in on how it can improve our work.

What do you think of artificial intelligence when it comes to your mind? All of us probably think of robots walking around us, resembling us and taking care of our desires. Are you feeling vaguely uneasy with this approach? It is estimated in a survey held in 2021 that 10.9 billion dollars will be spent on intelligent process automation (IPA). Doesn't it sound too interesting to you? Everything will be covered in this article.

We present a chatbot implementing a novel dialogue management approach based on logical inference. Instead of framing conversation a sequence of response generation tasks, we model conversation as a collaborative inference process in which speakers share information to synthesize new knowledge in real time. Our chatbot pipeline accomplishes this modelling in three broad stages. The first stage translates user utterances into a symbolic predicate representation. The second stage then uses this structured representation in conjunction with a larger knowledge base to synthesize new predicates using efficient graph matching. In the third and final stage, our bot selects a small subset of predicates and translates them into an English response. This approach lends itself to understanding latent semantics of user inputs, flexible initiative taking, and responses that are novel and coherent with the dialogue context.

We recently took a deep dive into how AdSkate makes digital advertising smarter with AI contextual targeting. Now we sit down with the founders to learn more about their stories. Tell me about the moment that sparked the idea for your product? Shreyas and Salil were exploring different applications for computer vision and machine learning models and they discussed the power of these technologies with Akaash. Akaash who was working at a large ad agency at the time suggested that the digital ad space right now is facing a lot of problems around privacy and brand safety aspects and this tech might just be the solution.

The task of image captioning aims to generate captions directly from images via the automatically learned cross-modal generator. To build a well-performing generator, existing approaches usually need a large number of described images, which requires a huge effects on manual labeling. However, in real-world applications, a more general scenario is that we only have limited amount of described images and a large number of undescribed images. Therefore, a resulting challenge is how to effectively combine the undescribed images into the learning of cross-modal generator. To solve this problem, we propose a novel image captioning method by exploiting the Cross-modal Prediction and Relation Consistency (CPRC), which aims to utilize the raw image input to constrain the generated sentence in the commonly semantic space. In detail, considering that the heterogeneous gap between modalities always leads to the supervision difficulty of using the global embedding directly, CPRC turns to transform both the raw image and corresponding generated sentence into the shared semantic space, and measure the generated sentence from two aspects: 1) Prediction consistency. CPRC utilizes the prediction of raw image as soft label to distill useful supervision for the generated sentence, rather than employing the traditional pseudo labeling; 2) Relation consistency. CPRC develops a novel relation consistency between augmented images and corresponding generated sentences to retain the important relational knowledge. In result, CPRC supervises the generated sentence from both the informativeness and representativeness perspectives, and can reasonably use the undescribed images to learn a more effective generator under the semi-supervised scenario.