If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
With ever-growing data generation and its usage, the demand for machine learning models is multiplying. As ML systems encompass algorithms and rich ML libraries, it helps analyze data and make decisions. There is no wonder that machine learning is gaining more visibility as ML applications are dominating almost every aspect of the modern-day world. With rapidly increasing exploration and adoption of this technology in businesses, it is setting the ground for ample employment opportunities. However, landing a career in this disruptive field, you must be well-equipped and familiar with some of the best machine learning tools to create efficient and functional ML algorithms. Here are the 10 best machine learning tools to look for in 2021.
Professor Bram van Ginneken - who has organised many of these challenge events - will highlight this approach in his ECR 2021 presentation "Benefits of AI challenges to clinical practice." In it, he will discuss AI challenges and their organisation, offer an understanding of what happens with the data collected, and point out algorithms which won AI challenges and made it to the clinic. In addition, the work Professor van Ginneken and his associates are doing in this field is also enabling the development of other algorithms, often in very specialist areas that would not normally attract the development interest of major vendors. Speaking ahead of the ECR virtual session, he said the challenge events create an opportunity to compare algorithms for particular tasks. With some 150 products for AI in radiology with CE certification for use in Europe, commercial sensitivity among vendors means the content of some algorithms remains unclear, said Professor van Ginneken, who is Professor of Medical Image Analysis at Radboud University Medical Center in The Netherlands.
The demand for triaging technologies like conversational bots has risen sharply as the pandemic reaches new peaks. Millions of patients wait at least two hours to see a health care provider, according to a study published by the U.S. Centers for Disease Control and Prevention (CDC). Tech giants like IBM, Facebook, and Microsoft have partnered with governments and private industry to roll out chatbot-based solutions in response, as have a number of startups. Companies like Current Health and Twistle have teamed up with Providence and other health care providers to pilot at-home health-tracking platforms. Even before the pandemic, nine in 10 seniors said they would prefer to stay in their homes over the next 10 years, highlighting the need for remote health monitoring solutions.
The AI Watch AI history timeline that was developed last year to provide an easy-to-grasp overview of the history of artificial intelligence has been updated with 2020 breakthroughs. The dataset of the selected breakthroughs is published on the JRC Data Catalogue. AI Watch invites you to download this dataset, use it and share with them any idea for improving it using the feedback form or contact them at EC-AI-WATCH@ec.europa.eu
EXOS is piloting the use of Intel's 3D Athlete Tracking (3DAT) technology to help the next generation of professional footballers reach their full potential. This year's hopefuls risk feeling unprepared after coming off such a disruptive year and will need all the help they can get to achieve their goals. Pose estimation algorithms are then applied to analyse the biomechanics of athletes' movements "Metrics that were previously unmeasurable by the naked eye are now being revealed with Intel's 3DAT technology. We're able to take that information, synthesize it and turn it into something tangible for our coaches and athletes. It's a gamechanger when the tiniest of adjustments can lead to real, impactful results for our athletes."
With the time and effort it requires, sometimes dating can feel like a job––but, unfortunately, saying that you're single does nothing for your résumé. Here are a few ways to adapt your dating experiences into professional, C.V.-worthy titles and descriptions. Selecting from a mix of seasoned stars and aspiring hopefuls, I judge the performance and competence of prospects vying for the role. They audition for me, and I insure that only the most talented move forward. I am in charge of seeking out and acquiring the best partnerships for the brand.
For the past few months, an independent board of technology experts has been closely tracking the new ways that AI and data have been used to counter and mitigate the effects of the COVID-19 pandemic in the UK; and now, they are lifting the veil on the good, the bad and the ugly of the past year in digital tech. The Center for Data Ethics and Innovation (CDEI) has released a new report diving deep into the 118 individual use-cases for AI and data-driven technologies that have been added to the organization's COVID-19 repository since last November. Spanning vastly different sectors and locations, the examples collated in the document provide a unique vision of the ways that technology can help in a time of crisis. From piloting drones to delivering medical supplies, to monitoring the behavior of residents in public transport during the easing of lockdown restrictions: if there is one observation that all experts will agree on, it is certainly that technology has been a central pillar in the support of the response to the pandemic. "While public attention largely centred on high-profile applications aimed at either suppressing the virus or coping with its effects, our research highlights the breadth of applications beyond these two use-cases," says the report.
Markets are subject to fads and the embedded-control sector is far from immune to them. In the 1990s, fuzzy logic seemed to be the way forward and microcontroller (MCU) vendors scrambled to put support into their offerings only to see it flame out. Embedded machine learning (ML) is seeing a far bigger feeding frenzy as established MCU players and AI-acceleration start-ups try to demonstrate their commitment to the idea, which mostly goes under the banner of TinyML. Daniel Situnayake, founding TinyML engineer at software-tools company Edge Impulse and co-author of a renowned book on the technology, says the situation today is very different to that of the 1990s. "The exciting thing about embedded ML is that machine learning and deep learning are not new, unproven technologies - they've in fact been deployed successfully on server-class computers for a relatively long time, and are at the heart of a ton of successful products. Embedded ML is about applying a proven set of technologies to a new context that will enable many new applications that were not previously possible."