In the age of the coronavirus, face masks have become a part of normal life. They’re a safety requirement in many places, and for some people, a fashion statement. But for facial recognition technology, they pose a major challenge.

… don a mask and stare at your iPhone or Android device to unlock it, and you quickly see the problem for facial recognition.

… Some companies assert that their technology isn’t affected by masks, and that artificial intelligence can still detect and identify people with a high accuracy rate, even when half the face is covered.  Read More

As a result of the COVID-19 pandemic, organisations have been forced to reckon with new challenges. This is especially true in the case of vertical organisations that regularly interact with a large customer base such as in the banking, financial services and insurance (BFSI) or healthcare sectors.

The spread of SARS-CoV-2 virus has forced lockdowns of countries and has rattled the global economy. Traditional ways of managing customer experiences need to be re-examined under current circumstances. A recent Uniphore survey conducted in the US showed that consumers, overwhelmingly, prefer to use voice over every other medium when contacting a company’s contact center. The survey also highlighted that the long hold times during calls to a contact center are a significant pain point for customers and that they feel frustrated and angry when put on hold. Read More

Modern conversational agents (chatbots) tend to be highly specialized — they perform well as long as users don’t stray too far from their expected usage. To better handle a wide variety of conversational topics, open-domain dialog research explores a complementary approach attempting to develop a chatbot that is not specialized but can still chat about virtually anything a user wants. Besides being a fascinating research problem, such a conversational agent could lead to many interesting applications, such as further humanizing computer interactions, improving foreign language practice, and making relatable interactive movie and videogame characters. Read More

#chatbots

We present Meena, a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations. This 2.6B parameter neural net-work is trained to minimize perplexity, an automatic metric that we compare against human judgement of multi-turn conversation quality.To capture this judgement, we propose a human evaluation metric called Sensibleness and Specificity Average (SSA), which captures key elements of good conversation. Interestingly, our experiments show strong correlation between perplexity and SSA. The fact that the best perplexity end-to-end trained Meena scores high on SSA (72% on multi-turn evaluation) suggests that a human-level SSA of 86%is potentially within reach if we can better optimize perplexity. Additionally, the full version of Meena (with a filtering mechanism and tuned decoding) scores 79% SSA, 23% higher in absolute SSA than existing chatbots that we evaluated. Read More

#chatbots

Chatbots are among the most trending technology for which the industry is excited to get in integrated. They get touted as the next rendition of applications, similar to an immense change in the correspondence business. Since Facebook has extended access to its messenger administration, it is enabling firms to achieve clients better through various APIs. Read More

#chatbots