Please note that DISQUS operates this forum. When you sign in to comment, IBM will provide your email, first name and last name to DISQUS. That information, along with your comments, will be governed by DISQUS' privacy policy. By commenting, you are accepting the DISQUS terms of service.

Finding the best way to get around a busy city is no easy task. Unless you know the place like the back of your hand, choosing between cars, public transport, bike and even scooter shares can be a daunting prospect – but IBM's Watson might be able to help. At MWC 2019, Seat and IBM announced Mobility Advisor, which uses Watson artificial intelligence (AI) to work out the best way to reach your destination – whether it's a train, ride-hailing service or an electric scooter. The tool's suggestions will take into account traffic reports, weather forecasts, and any events happening in the city that day, so you won't get caught in the rain riding a hire bike, or reach a train station at the same time as a crowd of sports fans. Mobility Advisor is currently in development, and is intended to run as a mobile app on 4G and 5G networks.

We introduce the new task of Acoustic Question Answering (AQA) to promote research in acoustic reasoning. The AQA task consists of analyzing an acoustic scene composed by a combination of elementary sounds and answering questions that relate the position and properties of these sounds. The kind of relational questions asked, require that the models perform non-trivial reasoning in order to answer correctly. Although similar problems have been extensively studied in the domain of visual reasoning, we are not aware of any previous studies addressing the problem in the acoustic domain. We propose a method for generating the acoustic scenes from elementary sounds and a number of relevant questions for each scene using templates. We also present preliminary results obtained with two models (FiLM and MAC) that have been shown to work for visual reasoning.

With Watson Machine Learning Accelerator you can drive faster time to results and accuracy, running in special AI hardware in the Cloud on On-Premises. WML Accelerator comes with SnapML library. We have developed an effi cient, scalable machine-learning library that enables very fast training of generalized linear models. We have demonstrated that our library can remove the training time as a bottleneck for machine-learning workloads, paving the way to a range of new applications. For instance, it allows more agile development, faster and more fine-grained exploration of the hyper-parameter space, enables scaling to massive datasets and makes frequent retraining of models possible in order to adapt to events as they occur.

Question Answering (QA), as a research field, has primarily focused on either knowledge bases (KBs) or free text as a source of knowledge. These two sources have historically shaped the kinds of questions that are asked over these sources, and the methods developed to answer them. In this work, we look towards a practical use-case of QA over user-instructed knowledge that uniquely combines elements of both structured QA over knowledge bases, and unstructured QA over narrative, introducing the task of multi-relational QA over personal narrative. As a first step towards this goal, we make three key contributions: (i) we generate and release TextWorldsQA, a set of five diverse datasets, where each dataset contains dynamic narrative that describes entities and relations in a simulated world, paired with variably compositional questions over that knowledge, (ii) we perform a thorough evaluation and analysis of several state-of-the-art QA models and their variants at this task, and (iii) we release a lightweight Python-based framework we call TextWorlds for easily generating arbitrary additional worlds and narrative, with the goal of allowing the community to create and share a growing collection of diverse worlds as a test-bed for this task.

Multimodal attentional networks are currently state-of-the-art models for Visual Question Answering (VQA) tasks involving real images. Although attention allows to focus on the visual content relevant to the question, this simple mechanism is arguably insufficient to model complex reasoning features required for VQA or other high-level tasks. In this paper, we propose MuRel, a multimodal relational network which is learned end-to-end to reason over real images. Our first contribution is the introduction of the MuRel cell, an atomic reasoning primitive representing interactions between question and image regions by a rich vectorial representation, and modeling region relations with pairwise combinations. Secondly, we incorporate the cell into a full MuRel network, which progressively refines visual and question interactions, and can be leveraged to define visualization schemes finer than mere attention maps. We validate the relevance of our approach with various ablation studies, and show its superiority to attention-based methods on three datasets: VQA 2.0, VQA-CP v2 and TDIUC. Our final MuRel network is competitive to or outperforms state-of-the-art results in this challenging context. Our code is available: https://github.com/Cadene/murel.bootstrap.pytorch

We introduce GQA, a new dataset for real-world visual reasoning and compositional question answering, seeking to address key shortcomings of previous VQA datasets. We have developed a strong and robust question engine that leverages scene graph structures to create 22M diverse reasoning questions, all come with functional programs that represent their semantics. We use the programs to gain tight control over the answer distribution and present a new tunable smoothing technique to mitigate language biases. Accompanying the dataset is a suite of new metrics that evaluate essential qualities such as consistency, grounding and plausibility. An extensive analysis is performed for baselines as well as state-of-the-art models, providing fine-grained results for different question types and topologies. Whereas a blind LSTM obtains mere 42.1%, and strong VQA models achieve 54.1%, human performance tops at 89.3\%, offering ample opportunity for new research to explore. We strongly hope GQA will provide an enabling resource for the next generation of models with enhanced robustness, improved consistency, and deeper semantic understanding for images and language.

India could emerge as the third-largest market in the Asia-Pacific (APAC) region for IBM's artificial intelligence (AI)-powered workforce automation solution, launched in November last year. The Armonk-based software services giant expects large-sized and mid-sized enterprises from sectors such as banking, insurance and manufacturing to be among the first adopters of the solution. The solution, dubbed the Talent and Transformation suite of services, is one among several that have come out of IBM's global AI platform, Watson. "India is one of the largest markets for the solution in terms of opportunity after Australia and Singapore (in the APAC region)," Lula Mohanty, general manager for APAC at IBM Global Business Services, told TechCircle. "Only five per cent of chief executive officers (CEOs) think that they have embarked on a transformation journey, especially when it comes to human resources core functions and only 24% of CHROs (chief human resources officers) think that they have a lot of work to do in terms of improving their core functions. This is a positive change in terms of rising awareness in the country," she added.

Folks that don't do much of it are often astounded about how quickly costs escalate and how much the process can cost. While the trial itself can cost upwards of $50K, just getting to trial with all the back and forth between the attorneys can cost several times that. A general rule of thumb is that unless the judgment is reasonably likely to be over $100K and include attorney's fees, you'll probably end up in the hole even if you win. Litigation was one of the initial target industries for IBM's advanced artificial intelligence (AI) platform Watson because litigation was so well defined and well documented. The promise was a significant reduction in costs for those bringing or defending against lawsuits and a far better way of determining if it was economically viable to bring or defend against the action to begin with.

We propose a new class of probabilistic neural-symbolic models, that have symbolic functional programs as a latent, stochastic variable. Instantiated in the context of visual question answering, our probabilistic formulation offers two key conceptual advantages over prior neural-symbolic models for VQA. Firstly, the programs generated by our model are more understandable while requiring lesser number of teaching examples. Secondly, we show that one can pose counterfactual scenarios to the model, to probe its beliefs on the programs that could lead to a specified answer given an image. Our results on the CLEVR and SHAPES datasets verify our hypotheses, showing that the model gets better program (and answer) prediction accuracy even in the low data regime, and allows one to probe the coherence and consistency of reasoning performed.