"Meta has done a great job having a breadth of different things they support, like text-to-speech, speech-to-text, even automatic speech recognition," says Chetan Jaiswal, a professor of computer science at Quinnipiac University, who was not involved in the research. "The mere number of languages they are supporting is a tremendous achievement." Human translators are still a vital part of the translation process, the researchers say in the paper, because they can grapple with diverse cultural contexts and make sure the same meaning is conveyed from one language into another. This step is important, says Lynne Bowker, Canada Research Chair in Translation, Technologies and Society at Université Laval in Quebec, who didn't work on Seamless. "Languages are a reflection of cultures, and cultures have their own ways of knowing things," she says.

Assigning appropriate industry tag(s) to a company is a critical task in a financial institution as it impacts various financial machineries. Yet, it remains a complex task. Typically, such industry tags are to be assigned by Subject Matter Experts (SME) after evaluating company business lines against the industry definitions. It becomes even more challenging as companies continue to add new businesses and newer industry definitions are formed. Given the periodicity of the task it is reasonable to assume that an Artificial Intelligent (AI) agent could be developed to carry it out in an efficient manner. While this is an exciting prospect, the challenges appear from the need of historical patterns of such tag assignments (or Labeling). Labeling is often considered the most expensive task in Machine Learning (ML) due its dependency on SMEs and manual efforts. Therefore, often, in enterprise set up, an ML project encounters noisy and dependent labels. Such labels create technical hindrances for ML Models to produce robust tag assignments. We propose an ML pipeline which uses semantic similarity matching as an alternative to multi label text classification, while making use of a Label Similarity Matrix and a minimum labeling strategy. We demonstrate this pipeline achieves significant improvements over the noise and exhibit robust predictive capabilities.

This paper presents a case-based reasoning (CBR) application for discovering similar patients with non-specific musculoskeletal disorders (MSDs) and recommending treatment plans using previous experiences. From a medical perspective, MSD is a complex disorder as its cause is often bounded to a combination of physiological and psychological factors. Likewise, the features describing the condition and outcome measures vary throughout studies. However, healthcare professionals in the field work in an experience-based way, therefore we chose CBR as the core methodology for developing a decision support system for physiotherapists which would assist them in the process of their co-decision making and treatment planning. In this paper, we focus on case representation and similarity modeling for the non-specific MSD patient data as well as we conducted initial experiments on comparing patient profiles.

Only 20 per cent of the 530 million Indian internet users consume content in English. According to a recent report by research and consulting firm RedSeer, 260 million Indian users are "monetizable". Nearly 210 million of these users, with an annual spending power of $300 billion, prefer digital content in vernacular languages. "India has added Internet users at 8X speed in the last 10 years driven by small towns and villages, not by large cities," the report stated. Most of such people used to go to an agent when they wanted to book a train ticket for their next Vaishno Devi trip with their family, wanted an instance plan and wanted someone to help them with a purchase.

This paper presents a case-based reasoning (CBR) application for discovering similar patients with non-specific musculoskeletal disorders (MSDs) and recommending treatment plans using previous experiences. From a medical perspective, MSD is a complex disorder as its cause is often bounded to a combination of physiological and psychological factors. Likewise, the features describing the condition and outcome measures vary throughout studies. However, healthcare professionals in the field work in an experience-based way, therefore we chose CBR as the core methodology for developing a decision support system for physiotherapists which would assist them in the process of their co-decision making and treatment planning. In this paper, we focus on case representation and similarity modeling for the non-specific MSD patient data as well as we conducted initial experiments on comparing patient profiles.

Today's artificial intelligence-based applications are changing people's lives in ways that often go unnoticed. A new study by Accenture, however, reports that AI could dramatically boost economic growth and productivity by up to 40% in 2035, prompting many to sit up and take notice of the burgeoning industry. Already advances in AI-powered technologies like robots, virtual assistants and augmented reality have stimulated fervent interest from companies such as Google, IBM, Apple, Facebook and Microsoft. And while it's hard to pinpoint the exact path that it will take, with global tech giants making huge strides in this sector, AI startups are arguably the most sought-after in today's global economy. The trend is catching on in India as well.

Indians living in Tier-2 cities are driving mobile penetration and represent largely untapped markets for many app developers. India is one of the app-friendliest countries around, having this year surpassed the U.S. with the highest number of downloads. However, while India's most popular apps largely cater to urban dwellers, there is a growing segment of Indians residing in smaller towns and cities that are responsible for the country's rapid internet growth. This became a focal point for four Indian entrepreneurs, who decided to develop a platform for users living in India's Tier-2 cities and towns that speak a variety of regional languages. The startup aims to be a one-app solution that will communicate with users in their preferred language and help to complete financial transactions.

Bangalore-based Niki.ai, which runs an artificial intelligence-powered personal assistant, has raised $2 million (about Rs 13 crore) in a Series A round of funding from San Francisco-based fund SAP.iO and existing investor Unilazer Ventures. VCCircle had exclusively reported on this development last month. Haresh Chawla of private equity firm True North, and Arihant Patni of Hive Technologies also invested, besides some US- and Germany-based investors, the company said on Wednesday. Software giant SAP launched SAP.iO with an initial investment of $35 million in March this year. The fund seeks to make early-stage investments in software startups with an aim to expand the SAP ecosystem.

Globally, nearly $2 billion in online sale were performed exclusively through chatbots last year. The march of chatbots and personal assistants into conversational commerce (defined as the intersection of messaging apps and shopping) is now a thing, and it looks like it is here to stay. Indian brands large and small are adopting chatbots, harnessing the power and convenience of these digital capabilities. "AI is changing the way we interact with technologies across multiple industries. In a fast-growing market such as India, AI helps making technology-based companies more efficient," says Sachin Jaiswal, cofounder of Niki.ai.

Artificial intelligence (AI) is being counted (pdf) among the hottest startup sectors in India this year, but the highly specialised space is struggling to grow due to the lack of a primary input: engineers. "Forget getting people of our choice, we don't even get applications when we advertise for positions for our AI team," said 25-year-old Tushar Chhabra, co-founder of Cron Systems, which builds internet of things (IOT)-related solutions for the defence sector. "It's as if people are scared of the words'artificial intelligence.' They start freaking out when we ask them questions about AI." India has over 170 startups focused purely on AI, which have together raised over $36 million. The sector has received validation from marquee investors like Sequoia Capital, Kalaari Capital, and business icon Ratan Tata.