Now your iPhone or other Apple device can help you determine if you have symptoms associated with the coronavirus. You can simply ask Siri about symptoms of the coronavirus – "Siri, what are the symptoms of the coronavirus?" The assistant will ask you whether you certain symptoms such as fever, dry cough or trouble breathing and if you have been in contact with someone who might have contracted the virus. Even if Siri assesses that you are at lower risk, the assistant will offer some advice on hand washing and social distancing. Answers come from the U.S. Public Health Service and the Centers for Disease Control and Prevention.

From intelligent personal assistants to home robots, technology once thought of as a sci-fi dream is now embedded into everyday life. But this leap from dream to reality didn't happen overnight. There is no one'eureka' moment in a field as vast as AI. Rather, the technology we enjoy today is a result of countless milestones in artificial intelligence, delivered by countless forgotten people across a countless range of projects. So, let's pay homage to some of that work.

Automatic Speech Recognition (ASR) has increased in popularity in recent years. The evolution of processor and storage technologies has enabled more advanced ASR mechanisms, fueling the development of virtual assistants such as Amazon Alexa, Apple Siri, Microsoft Cortana, and Google Home. The interest in such assistants, in turn, has amplified the novel developments in ASR research. However, despite this popularity, there has not been a detailed training efficiency analysis of modern ASR systems. This mainly stems from: the proprietary nature of many modern applications that depend on ASR, like the ones listed above; the relatively expensive co-processor hardware that is used to accelerate ASR by big vendors to enable such applications; and the absence of well-established benchmarks. The goal of this paper is to address the latter two of these challenges. The paper first describes an ASR model, based on a deep neural network inspired by recent work in this domain, and our experiences building it. Then we evaluate this model on three CPU-GPU co-processor platforms that represent different budget categories. Our results demonstrate that utilizing hardware acceleration yields good results even without high-end equipment. While the most expensive platform (10X price of the least expensive one) converges to the initial accuracy target 10-30% and 60-70% faster than the other two, the differences among the platforms almost disappear at slightly higher accuracy targets. In addition, our results further highlight both the difficulty of evaluating ASR systems due to the complex, long, and resource intensive nature of the model training in this domain, and the importance of establishing benchmarks for ASR.

Kyruus, the leader in provider search and scheduling solutions for health systems, and GYANT, the leading artificial intelligence-enabled virtual assistant for healthcare, announced a new partnership to help health systems enhance consumer access and self-service on their websites. Currently, Kyruus delivers industry-defining provider search and scheduling solutions that help health systems match patients with the right providers across their enterprise-wide access points. The two companies will join forces to combine the power of Kyruus' routing and scheduling platform with GYANT's chat-based virtual assistant to help consumers find and book appropriate care through a conversational experience. The partnership will enable health systems to capitalize on the comprehensive provider directory and direct scheduling integrations they put into place with Kyruus to enhance the scope of services they offer via GYANT's virtual assistant. Health systems working with Kyruus utilize the KyruusOne provider data management platform – including its extensive clinical taxonomy – to build rich provider profiles and a system-wide view of appointment availability.

Automated cars are in our future--and starting to be in our present. In 2014, the Society of Automotive Engineers (SAE) published the first version of a taxonomy for degree of automation in vehicles from Level 0 (not automated) to Level 5 (fully automated, no human intervention necessary).8 Since then, this taxonomy has gained wide acceptance--to the point where everyone from the U.S. government (used by the NHTSA5) to auto manufacturers to the popular press are talking in terms of "skipping level 3" or "everyone wants a level 5 car."1 As technology gets developed and improved, having an accepted taxonomy helps ensure people can talk to each other and know they are talking about the same thing. It is time for one of our computing organizations (perhaps ACM?) to develop an analogous taxonomy for automated assistants. With Siri, Alexa, Cortana, and cohorts selling in the "tens of millions"2 and with more than 20 competitors on the market,7 having an easily understandable taxonomy will help practitioners and end users alike.

The way people receive and consume news and entertainment has changed drastically nowadays with features such as personalized content coming into play. Other technologies have also changed the entire ball game regarding content creation and distribution. Although this subset of AI seemed to thrive, the growth was quite stagnant in the education industry, but not of late. There are many applications of AI in the education industry that have transformed the perspective of many students by enabling smart learning. So, how has AI changed the education industry and what is the future in this?

Artificial intelligence technology has continued to grow in recent years, stunning the world with its latest innovations. But, some are admittedly growing weary about AI and its continuous growth. With talk of robots one day replacing humans for labor, concerns of an increasingly tech dependent world grow stronger. A report from Oxford researchers stated that 47% of American jobs will be at risk by 2030 because of automation. However, AI is truly changing the world - providing innovation that can change how we approach healthcare, the environment, and the day to day act of living.

Television shows of the 1960's like The Jetsons predicted that the 21st century would be filled with flying cars, and airborne robots would be a part of our everyday lives. October 21st, 2015 marked the point in time in which Marty McFly (Michael J. Fox) traveled to in Back to the Future Part II, the 1989 sequel to the time-travelling classic. The future he found was one which had captured the imagination of millions -- instead today, we live in a world dominated by live streaming, smartphones and social networks, not flying cars or hover boards (maybe, because is this really a hover board?). Within the span of 10 short years, or perhaps even less, service apps like Uber, Lyft, DoorDash, AirBnB and others have spawned millions of users, and can be found on almost everyone's smart phone. Personal assistants like Siri and Alexa have entered many of our lives. It would be terribly naive for anyone to say that the world hasn't changed in the last 10 years.

It's 2020, and most will admit we aren't where some scientists and experts thought we would be when it comes to flying cars, teleportation, time travel, and other futuristic concepts. But the technology and computer capabilities we have today are light-years ahead of where we were even 20 years ago. Yet many of us are still weary of some of the most recent advancements – and this includes Artificial Intelligence (AI). Artificial Intelligence refers to computers or other machines developing the intelligence and capabilities of human cognition. Examples of this include speech recognition, problem-solving, learning, and planning1.

Although every business is different, even those in completely separate industries face some of the same long-standing problems. In recent years, artificial intelligence has become the technology that's well-positioned to solve many of these business challenges. Let's look at five key challenges businesses face and how AI-powered solutions from specific companies are addressing those obstacles. Handling more digital and mobile transactions gives customers what they want. However, it may also give criminals what they want -- that is, an opportunity to grab sensitive personal and financial data.