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Conversational AI -- Key Technologies and Challenges -- Part 2


Follow up on my previous post discussing the key technologies around the conversational AI solution, I will be dive into the typical challenges the AI Engineer team would encounter when building a virtual agent or a chatbot solution for your clients or customers. Let firstly define the scope and goal of the conversational application. The conversational agents can be categorized into two main streams. The typical agents for Open Domain Conversation are Siri, Google Assistant, BlenderBot from Facebook, Meena from Google. Users can start a conversation without a clear goal, and the topics are unrestricted.



This implements a self-contained compiler for a linear algebra set of operations inspired by XLA HLO IR using MLIR components. It is designed to provide an end-to-end flow independent of TensorFlow and XLA, but usable inside of these projects. Coding practice and conventions in this repository follow the MLIR Developer Guide in this repo as part of the intent to act as an incubator for technology to upstream. These instructions work on Linux, you may have to adjust for your plaform. Again this is something to do every time you pull from this repository and the LLVM revision changes.

AI Helping to Transform Education in Pandemic Era - AI Trends


The impact of the COVID-19 pandemic on education has been profound, with new ways of thinking about how best to teach students reverberating in institutions of higher learning, K-12 classrooms and in the business community. The role of AI is central to the discussion on every level. For the K-12 classroom, teachers are thinking about how to use AI as a teaching tool. For example, Deb Norton of the Oshkosh Area school district in Wisconsin, was asked several years ago by the International Society for Technology in Education to lead a course on the uses of AI in K-12 classrooms, according to a recent account in Education Week. The course includes sections on the definition of artificial intelligence, machine learning, voice recognition, chatbots and the role of data in AI systems.

Stanford Launches Online Program on AI in Healthcare


The Stanford Center for Health Education has launched an online program on Artificial Intelligence in Healthcare. Designed for technology professionals, computer scientists, and healthcare providers, the program aims to advance the delivery of patient care and improve global health outcomes through artificial intelligence and machine learning. The online program will be taught by faculty from Stanford Medicine. The program's goal is to foster a common understanding of the potential for AI to safely and ethically improve patient care. "Effective use of AI in healthcare requires knowing more than just the algorithms and how they work," says Nigam Shah, associate professor of medicine and biomedical data science, the faculty director of the new program.

7 takeaways on AI, automation in local government


As concerns surrounding the government technology workforce remain, robotic process automation presents a big opportunity to relieve the pressure of losing workers. The software can perform repetitive tasks, like data entry, much faster than people and dramatically improve the efficiency of workflows. "[RPA] seems like our best bet in the immediate future," one CIO said. "It's process-based and is more like an automation of a current thing we already do as opposed to implementing some new, flashy tool." The CIO also expressed interest in the fail-safes that can be installed with RPA, ensuring that human operators are kept in the loop.

Why You Should Care About AI (Even If You Don't Want To)


AI is one of the hottest buzzwords right now. And, while almost every media outlet is talking about AI, most people do not even know what it is and what exactly it can do. AI is a mystery technology. Some of the messages in the media warn that it could take all of our jobs, replacing humans completely in the workforce. While both messages grab your attention, unsurprisingly, neither is entirely true.

Massive Growth in Artificial Intelligence in Precision Medicine Market Set to Witness Huge Growth by 2026


Artificial Intelligence in Precision Medicine Market research is an intelligence report with meticulous efforts undertaken to study the right and valuable information. The data which has been looked upon is done considering both, the existing top players and the upcoming competitors. Business strategies of the key players and the new entering market industries are studied in detail. Well explained SWOT analysis, revenue share and contact information are shared in this report analysis. "Artificial Intelligence in Precision Medicine Market is growing at a High CAGR during the forecast period 2020-2026. The increasing interest of the individuals in this industry is that the major reason for the expansion of this market".

Learn the world's most-spoken languages with these virtual courses


Don't let the Gen Zers fool you into thinking that the world's most spoken language is memes. While the idea of interacting by way of Spongebob clips and unflattering celebrity photos is fun, it's still not an acceptable way of communication outside of social media -- at least not yet, anyway. The list of the world's most spoken languages includes Mandarin, English, Spanish, Arabic, and Hindi, and learning them is incredibly important. Not only can gaining fluency in these languages open you up to more career opportunities, but it can also warrant you a hefty salary increase. If you want to be well-versed in these languages without using an app that pressures you every second of the day, you can pick up the World's Most Spoken Languages Bundle to beef up your multilingual skills.

Origami Risk, AI firm partner - Business Insurance


Origami Risk LLC and Gradient A.I. Corp. have formed a partnership allowing Gradient's claims and policy modeling capabilities and predictive analytics resources to be used on Origami's digital platform, the companies said in a joint release Tuesday. Insurers, third-party administrators, risk pools, and self-insured organizations will be able to access Gradient's proprietary data sets of millions of claims and policies, which are integrated with the Origami platform's workflow, reporting and digital engagement tools, the statement said. The Gradient tools can be applied to policy underwriting and claims adjusting processes, such as enabling claim teams to focus greater attention on claims with a high probability of becoming significant cost-drivers, the statement said. "Our collaboration with Gradient AI offers insurers, risk pools and large self-administered plans using our platform ready access to" Gradient's tools, Robert Petrie, CEO of Origami Risk, said in the statement.

Who Does the Machine Learning and Data Science Work?


A survey of over 19,000 data professionals showed that nearly 2/3rds of respondents said they analyze data to influence product/business decisions. Only 1/4 of respondents said they do research to advance the state of the art of machine learning. Different data roles have different work activity profiles with Data Scientists engaging in more different work activities than other data professionals. We know that data professionals, when working on data science and machine learning projects, spend their time on a variety of different activities (e.g., gathering data, analyzing data, communicating to stakeholders) to complete those projects. Today's post will focus on the broad work activities (or projects) that make up their roles at work, including "Build prototypes to explore applying machine learning to new areas" and "Analyze and understand data to influence product or business decisions".