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) …
Advanced agriculture technology like Harvest CROO Robotics' automated strawberry harvester are poised to take on the heavy lifting for farmers. "Necessity is the mother of invention," so the saying goes. It's certainly appropriate when referring to advancements made in agriculture technology. The lack of available farm labor alone has given rise to automated smart harvesters. In a recently published article, two University of Florida researchers say robots and information technology will be the rule and no longer the exception on farms in the coming years.
The truth is that you can code in almost any software, from prompt command to Windows notepad, but you may also want a proper programming environment which combines coding facility with a debugging environment. So why would or do you choose a traditional IDE instead of, for example, a notepad? The answer would be practicality. For instance, imagine that you are coding in any text editor like Windows notepad. When your code is ready, you'll need to run it.
This week's Opinion Piece is written by Antonio Grasso, the CEO and Founder of Digital Business Innovation Srl, as well as the CTO of Think Digital Srl. Business Process Automation or BPA, aims to automate an enterprise process using technology; the main goal is to optimize process execution, saving time and costs. The injection of technology in workflow helps to fulfil work better, boosting the effectiveness of enterprises. In the past few years we've seen the advancement of a new technology called Robotic Process Automation, or RPA, used to automate recurring human software-tasks like responding to an email, clicking on some buttons or filling some web forms; something like a digital monkey that emulates human actions. In fact, the most common RPA is based on a registration of tasks (press this button – if this then that – fill this form etc) and the subsequent emulations of those tasks.
You're probably familiar with the terminology, but do you understand what data science is and how it fits in the big technological picture? Discover its secrets with the Machine Learning and Data Science Certification Training Bundle, marked down to just $35 for a limited time. Use code PREZDAY60 today and get it for just $14. Machine learning and data science are popular areas of expertise these days. It's what allows us to program a computer to adjust its behavior based on the data it collects through experience.
If you thought the bring-your-own-device (BYOD) experience was a challenge for companies, brace yourself. The mid-2000s brought waves of heterogeneous, non-sanctioned devices into the network. By 2009, workers had made it clear that they preferred BYOD, as CIOs began feeling the pressure of personal devices flooding the workplace. The result has been the creation of so-called "shadow IT" -- projects (applications and systems) managed outside of, and without the knowledge of, the IT department. The BYOD phenomenon went hand in hand with the adoption of non-sanctioned, cloud-based software as a service (SaaS) applications to address a line of business needs.
Echoing Mr Topol's suggestion of greater stakeholder involvement in the development of medical applications, the role of low-code and no-code applications will come to the fore. In my previous blog, I spoke about low-code/no-code software, whereby staff drag-and-drop application components, connect them together and create a mobile or web app. These tools can empower staff in every department to be able to create their own internal applications to suit their needs, without writing any code or needing to bother the already stretched IT departments. These so-called "citizen developers", will have the means to create robust applications in a safe, scalable environment that has all of the tooling and procedures to ensure appropriate security, testing and sign-off as part of the NHS's Digital Transformation program.
Machine learning has already captured the industry's attention and driven rapid changes in ad technology, which is the least it could do given the amount of hype it has received. What's even more fascinating, though, is that the pace of the ML revolution is only increasing, and the real change has barely begun. Smart use of ML is now a differentiator and competitive advantage, but it is about to become an absolute requirement to remaining relevant in ad tech. While there continues to be breakthroughs in core ML research, it is not the academic vanguard that is driving rapid change in our industry, but rather the broadening base of knowledge among nonspecialist engineers. Just a few years ago machine learning was largely restricted to a small group of experts -- a handful of Ph.D.s from a handful of top universities.
The DMIR Research Group is a highly interdisciplinary group of researchers led by Prof. Andreas Hotho. We are active in machine learning and data science research with multiple applications domains like text analysis, environmental sensing and user analysis, recommender systems and anomaly detection (see https://dmir.org In all of these areas, we deal with temporally variable data, thus necessitating the application, development and research of methods in the area of time series analysis. We are especially interested in combining the analysis of temporal data with formally represented and existing knowledge. The position is a typical postdoc position for qualification with a maximum duration of 6 years.
I have worked on the problem of open-sourcing Machine Learning versus sensitivity for a long time, especially in disaster response contexts: when is it right/wrong to release data or a model publicly? This article is a list of frequently asked questions, the answers that are best practice today, and some examples of where I have encountered them. The criticism of OpenAI's decision included how it limits the research community's ability to replicate the results, and how the action in itself contributes to media fear of AI that is hyperbolic right now. It was this tweet that first caught my eye. Anima Anandkumar has a lot of experience bridging the gap between research and practical applications of Machine Learning.
My name is Andrew Zaldivar, and I am a Developer Advocate for Google AI in SF. Specifically, I work on a research-based team focused on developing and promoting socio-technical strategies that can advance positive outcomes from AI long-term. In my role, I act as a servant to the public's interest in developing ethical AI systems. I completed my doctorate degree in cognitive neuroscience, but my studies were complemented with informatics, psychology and data science, which helped prepare me to examine the interplay of people and technology and what it means for our future. To help developers take on the challenge of building fairness into their machine learning models, I helped develop a short, self-study Fairness Module that is part of our Machine Learning Crash Course.