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) …
Speech Recognition is the ability of a machine or program to identify words and phrases in spoken language and convert them to textual information. You have probably seen it on Sci-fi, and personal assistants like Siri, Cortana, and Google Assistant, and other virtual assistants that interact with through voice. In order to understand your voice these virtual assistants need to do speech recognition. Speech Recognition is a complex process, so I'm not going to teach you how to train a Machine Learning/Deep Learning Model to do that. Instead, I will instruct you how to do it using google speech recognition API.
AI has been part of our imaginations and simmering in research labs since a handful of computer scientists rallied around the term at a Research Workshop at Dartmouth College in 1956 and birthed the field of AI. Back in 1956, the dream of AI pioneers such as John McCarthy was to construct complex machines that possessed characteristics of human intelligence. However, general AI machines that replicate human senses, human reasoning, and think as we do are still mostly constrained to Hollywood and science fiction novels. AI today is, however, able to perform specific, comparably narrow tasks as well as, or sometimes better than, we humans can. Examples of narrow AI include applications such as classification of pathology from X-ray imagery, identification of people in Facebook photos via facial recognition, or your spam filters in Gmail.
This month saw the European Conference on AI (ECAI 2020) go digital. Included in the programme were five plenary talks. In this article we summarise the talk by Professor Carme Torras who gave an overview of her group's work on assistive AI, and talked about the ethics of this field. Carme is based at the Institut de Robòtica i Informàtica Industrial (CSIC-UPC) in Barcelona. Her lab includes an assisted living facility where the team can test their robots in real-life situations.
TL;DR: Create your own game with the Build The Legend of Zelda Clone in Unity3D and Blender course for $35, an 82% savings as of Sept. 30. If you're curious to know what makes Zelda a hit among gamers, you may want to consider finding out how it was created in the first place. The Build The Legend of Zelda Clone in Unity3D and Blender course will show what makes a game like Zelda tick, and give you an intro to game development and design to boot. You'll get a shot at recreating The Legend of Zelda -- a Nintendo classic. Taught by John Bura, a seasoned game programmer and educator, this course is designed to help you develop a game from scratch using Unity (a game engine) and Blender (an open-source 3D computer graphics software toolset).
Feature engineering is the process of using domain knowledge of the data to transform existing features or to create new variables from existing ones, for use in machine learning. Data in its raw format is almost never suitable for use to train machine learning algorithms. Instead, data scientists devote a substantial amount of time to pre-process the variables to use them in machine learning. As you can see, feature engineering is an umbrella term that includes multiple techniques to perform everything from filling missing values, to encoding categorical variables, to variable transformation, to creating new variables from existing ones. In this post, I highlight the main feature engineering techniques to process the data and leave it ready to use for machine learning. I describe what each technique entails, and say a few words about when we should use each technique.
At the beginning of the artificial intelligence (AI)/machine learning (ML) era, the expectations are high, and experts foresee that AI/ML shows potential for diagnosing, managing and treating a wide variety of medical conditions. However, the obstacles for implementation of AI/ML in daily clinical practice are numerous, especially regarding the regulation of these technologies. Therefore, we provide an insight into the currently available AI/ML-based medical devices and algorithms that have been approved by the US Food & Drugs Administration (FDA). We aimed to raise awareness of the importance of regulatory bodies, clearly stating whether a medical device is AI/ML based or not. Cross-checking and validating all approvals, we identified 64 AI/ML based, FDA approved medical devices and algorithms. Out of those, only 29 (45%) mentioned any AI/ML-related expressions in the official FDA announcement. The majority (85.9%) was approved by the FDA with a 510(k) clearance, while 8 (12.5%) received de novo pathway clearance and one (1.6%) premarket approval (PMA) clearance. Most of these technologies, notably 30 (46.9%), 16 (25.0%), and 10 (15.6%) were developed for the fields of Radiology, Cardiology and Internal Medicine/General Practice respectively. We have launched the first comprehensive and open access database of strictly AI/ML-based medical technologies that have been approved by the FDA. The database will be constantly updated.
VMware makes software that helps businesses get more work out of data center servers by slicing physical machines into "virtual" ones so that more applications can be packed onto each physical machine. Its tools are commonly used by large businesses that operate their own data centers as well as businesses that use cloud computing data centers. For many years, much of VMware's work focused on making software work better with processors from Intel Corp, which had a dominant market share of data centers. In recent years, as businesses have turned to AI for everything from speech recognition to recognizing patterns in financial data, Nvidia's market share in data centers has been expanding because its chips are used to speed up such work. VMware's software tools will work smoothly with Nvidia's chips to run AI applications without "any kind of specialized setup," Krish Prasad, head of VMware's cloud platform business unit, said during a press briefing.