Aggressive marketing language can often drive a lack of clarity around technical concepts. Somewhat surprising, is that confusion between RPA and AI is high even among organizations that have automation in place and have likely gone through the diligence and/or request for proposal (RFP) process to select their automation solution. More than 50 percent of CFOs and revenue cycle leaders in organizations that are actively using automation say they consider RPA to be a form of artificial intelligence. "RPA based bots can be good tools for automating simple, discrete, and static tasks," said Varun Ganapathi, co-founder and Chief Technology Officer at Alpha Health. "AI and machine learning based technologies are capable of automating much more complex tasks that require some degree of judgment or subject matter expertise. Additionally, AI and machine learning based solutions can have the ability to navigate changes or new variables without the need to reprogram or recode a bot every time something changes."

A British artificial intelligence startup that has helped Sky News and YouTube stars translate their video content into foreign languages has secured £8m in funding to expand its speech-dubbing venture. Papercup, which was founded three years ago, attracted early investors including William Tunstell-Pedoe, whose business was acquired by Amazon to help develop Alexa, and Uber's former artificial intelligence (AI) chief Zoubin Ghahramani. Now it is targeting the millions of hours of content companies such as Netflix need to translate. "Most of the world's videos – billions of hours of content – are shackled to a single language," said Jesse Shemen, co-founder and chief executive of Papercup. "And that's for a simple reason, quality dubbing is prohibitively expensive and time-consuming, so current solutions only work for a select group of deep-pocketed content owners.

At a time when artificial intelligence algorithms are increasingly prevalent in our everyday lives (recruitment, customer relations, recommending content, banking and insurance, etc.), it is vital for Orange to ensure that the entire data value chain is managed responsibly and that potential discriminatory biases are identified and eliminated. The audit, carried out by Bureau Veritas, assessed Orange for its actions aimed at designing, developing and using inclusive artificial intelligence to promote diversity and avoid risks of discrimination before awarding the certification. The GEEIS-AI repository offers a way to raise awareness among the entire AI production chain, from design to operating the end product. In this way, a wide range of job lines and skills are at the service of the entire Orange ecosystem. This applies, in particular, to human resource management processes to guarantee they are not biased while encouraging diversity in AI job lines.

IBM also announced plans to commercialize IBM Research-developed'AI Factsheets' in Watson Studio in Cloud Pak for Data throughout the next year. Like nutrition labels for foods or information sheets for appliances, AI Factsheets are designed to provide information about a product's important characteristics. Standardizing and publicizing this information will help build trust in AI services across the industry.

Machine learning comes to the fore to help decipher valuable insights hidden in the vast heaps of data sets.

CONTENTshift is the accelerator program of the German Book Publishers and Printers Association. Below you will find more interviews from past batches. We used to record the interviews directly at Frankfurt Book Fair, but since it is canceled this year due to Corona, we resorted to remote only interviews. At the time of the recording, we did not know who won the final award. We will publish the exclusive interview with them as the last of our series this year.

It is notoriously difficult to control the behavior of artificial neural networks such as generative neural language models. We recast the problem of controlling natural language generation as that of learning to interface with a pretrained language model, just as Application Programming Interfaces (APIs) control the behavior of programs by altering hyperparameters. In this new paradigm, a specialized neural network (called a Neural Programming Interface or NPI) learns to interface with a pretrained language model by manipulating the hidden activations of the pretrained model to produce desired outputs. Importantly, no permanent changes are made to the weights of the original model, allowing us to re-purpose pretrained models for new tasks without overwriting any aspect of the language model. We also contribute a new data set construction algorithm and GAN-inspired loss function that allows us to train NPI models to control outputs of autoregressive transformers. In experiments against other state-of-the-art approaches, we demonstrate the efficacy of our methods using OpenAI's GPT-2 model, successfully controlling noun selection, topic aversion, offensive speech filtering, and other aspects of language while largely maintaining the controlled model's fluency under deterministic settings.

Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets without ground truth annotations by generating probabilistic labels using multiple noisy heuristics. This process can scale to large datasets and has demonstrated state of the art performance in diverse domains such as healthcare and e-commerce. One practical issue with learning from user-generated heuristics is that their creation requires creativity, foresight, and domain expertise from those who handcraft them, a process which can be tedious and subjective. We develop the first framework for interactive weak supervision in which a method proposes heuristics and learns from user feedback given on each proposed heuristic. Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels. We conduct user studies, which show that users are able to effectively provide feedback on heuristics and that test set results track the performance of simulated oracles. The performance of supervised machine learning (ML) hinges on the availability of labeled data in sufficient quantity and quality. However, labeled data for applications of ML can be scarce, and the common process of obtaining labels by having annotators inspect individual samples is often expensive and time consuming. Additionally, this cost is frequently exacerbated by factors such as privacy concerns, required expert knowledge, and shifting problem definitions. Weak supervision provides a promising alternative, reducing the need for humans to hand label large datasets to train ML models (Riedel et al., 2010; Hoffmann et al., 2011; Ratner et al., 2016; Dehghani et al., 2018). A recent approach called data programming (Ratner et al., 2016) combines multiple weak supervision sources by using an unsupervised label model to estimate the latent true class label, an idea that has close connections to modeling workers in crowd-sourcing (Dawid & Skene, 1979; Karger et al., 2011; Dalvi et al., 2013; Zhang et al., 2014).

Inc. company, and ViacomCBS announced an agreement that makes AWS the preferred cloud provider for ViacomCBS's global broadcast media operations. As part of the strategic agreement, ViacomCBS will migrate operations for its entire broadcast footprint, which spans 425 linear television channels and 40 global data and media centers, to the world's leading cloud – one of the first such large-scale transformations in the media and entertainment industry. The migration will enable ViacomCBS to drive greater efficiencies and cost savings, simplify access to content for its licensing partners, and reliably deliver new viewing experiences to consumers by broadcasting and streaming content on any device. ViacomCBS will leverage AWS's industry-leading infrastructure and comprehensive cloud capabilities, including serverless, containers, databases, media services, analytics, and machine learning, to build a cloud-based broadcast and media supply chain operating model. This new cloud-based hub will help the broadcaster spin up new channels faster, dynamically assemble live content to optimize delivery over any distribution channel, add image and video analysis to applications, and automate workflows.

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