Over the past decade, the demand for communication devices has increased among individuals with mobility and speech impairments. Eye-gaze tracking has emerged as a promising solution for hands-free communication; however, traditional appearance-based interfaces often face challenges such as accuracy issues, involuntary eye movements, and difficulties with extensive command sets. This work presents a multimodal appearance-based gaze-controlled virtual keyboard that utilises deep learning in conjunction with standard camera hardware, incorporating both synchronous and asynchronous modes for command selection. The virtual keyboard application supports menu-based selection with nine commands, enabling users to spell and type up to 56 English characters, including uppercase and lowercase letters, punctuation, and a delete function for corrections. The proposed system was evaluated with twenty able-bodied participants who completed specially designed typing tasks using three input modalities: (i) a mouse, (ii) an eye-tracker, and (iii) an unmodified webcam. Typing performance was measured in terms of speed and information transfer rate (ITR) at both command and letter levels. Average typing speeds were 18.3+-5.31 letters/min (mouse), 12.60+-2.99letters/min (eye-tracker, synchronous), 10.94 +- 1.89 letters/min (webcam, synchronous), 11.15 +- 2.90 letters/min (eye-tracker, asynchronous), and 7.86 +- 1.69 letters/min (webcam, asynchronous). ITRs were approximately 80.29 +- 15.72 bits/min (command level) and 63.56 +- 11 bits/min (letter level) with webcam in synchronous mode. The system demonstrated good usability and low workload with webcam input, highlighting its user-centred design and promise as an accessible communication tool in low-resource settings.

Neural language models are gaining popularity in real-life creative tasks such as text-adventure games, collaborative slogan writing, and even sports journalism, poetry and novel generation. Most such language models however provide limited interaction support for users, as control that goes beyond simple left-to-right text generation requires explicit training. To address this limitation, a team from Google Research has proposed Wordcraft, a text editor with a built-in AI-powered creative writing assistant. Wordcraft leverages few-shot learning and the natural affordances of conversation to support a variety of user interactions; and can help with story planning, writing and editing. The Wordcraft web interface comprises a traditional text editor augmented with a number of key commands for triggering requests to the AI assistant.

Most of today's chatbots are highly specific in their conversations (according to their domain of usage) and users can't afford to drift away from their expected use. They are not good with retaining context from past conversations, sometimes give meaningless, illogical responses and quite easily give the response, "I don't know". Open-domain chatbots are conversational agents that can chat about anything and have basic knowledge about the real world. In the research paper "Towards a Human-like Open-Domain Chatbot", Google introduced Meena. Meena is claimed to be the smartest chatbot, highly sensible and specific in its responses, unlike other chatbots.

First, Google's chatbot Meena and Facebook's chatbot Blender demonstrated that dialog agents can achieve close to human-level performance in certain tasks. Then, OpenAI's GPT-3 model made lots of people wonder whether Artificial General Intelligence (AGI) is already here. While we are still a long way off true AGI, conversations with GPT-3 based chatbots can be very entertaining. Are you interested to learn more about the latest research breakthroughs in Conversational AI? Check out our premium research summaries covering open-domain chatbots, task-oriented chatbots, dialog datasets, and evaluation metrics. Subscribe to our AI Research mailing list at the bottom of this article to be alerted when we release new summaries.

Google recently announced on it's AI blog the advent of a new age of chatbot. Meena has been trained on 2.6 billion parameters gathered from a vast variety of data sources but why is this important for conversational AI and conversational commerce? In any meeting, the ability to control and influence the conversation gives you power over the outcome. If conversations are automated, the best ones will deliver the best outcomes for those who create them. This is the reason there is such an arms race in this field.

Most providers/vendors say you need plenty of data to train a chatbot to handle your customer support or other queries effectively, But, how much is plenty, exactly? We take a look around and see how various bots are trained and what they use. Recent bot news saw Google reveal its latest Meena chatbot (PDF) was trained on some 341GB of data. Meena is "a multi-turn open-domain chatbot trained end-to-end on data mined and filtered from public domain social media conversations." The 38-page scientific paper highlights the advanced nature of Meena, but any business looking to train its bot faces that same opening question, how much training is enough?

Facebook has launched a new chatbot that it claims is able to demonstrate empathy, knowledge and personality. "Blender" was trained using available public domain conversations which included 1.5 billion examples of human exchanges. The social media giant said 49% of people preferred interactions with the chatbot, compared with another human. But experts say training the artificial intelligence (AI) using a platform such as Reddit has its drawbacks. Numerous issues arose during longer conversations.

Blender's ability comes from the immense scale of its training data. It was first trained on 1.5 billion publicly available Reddit conversations, to give it a foundation for generating responses in a dialogue. It was then fine-tuned with additional data sets for each of three skills: conversations that contained some kind of emotion, to teach it empathy (if a user says "I got a promotion," for example, it can say, "Congratulations!"); information-dense conversations with an expert, to teach it knowledge; and conversations between people with distinct personas, to teach it personality. The resultant model is 3.6 times larger than Google's chatbot Meena, which was announced in January--so big that it can't fit on a single device and must run across two computing chips instead. At the time, Google proclaimed that Meena was the best chatbot in the world.

The virtual assistants that inhabit our smartphones are helpful, sure, but they're not going to pass the Turing test any time soon. They're designed for understanding specific commands and actions like checking on restaurant reservations or getting updates on the weather, rather than, say, carrying on an in-depth conversation with a human. But chatbots could soon become far more loquacious thanks to Facebook, which this morning released a startlingly lifelike chatbot that it's been developing, dubbed Blender, as an open-source resource for AI research. Facebook has been pouring money and resources into its Natural Language Processing technologies for a few years now and those efforts appear to have paid off. The company claims that Blender is the single largest open-source chatbot created to date.

Welcome to the February edition of our best and favorite articles in AI that were published this month. We are a Paris-based company that does Agile data development. This month, we spotted among others, articles about AI that can diagnose breast cancer with higher accuracy than experts! Let's start, as usual, with the comic of the month: A recent evaluation of a AI system for breast cancer screening concludes that it is capable of surpassing human experts in breast cancer prediction. It is essential to identify breast cancer at earlier stages of the disease when treatment can be more successful.