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What is time-series data, and why are we building a time-series database (TSDB)?

#artificialintelligence

Like all good superheroes, every company has its own origin story explaining why they were created and how they grew over time. This article covers the origin story of QuestDB and frames it with an introduction to time series databases to show where we sit in that landscape today. Time series is a succession of data points ordered by time. These data points could be a succession of events from an application's users, the state of CPU and memory usage over time, financial trades recorded every microsecond, or sensors from a car emitting data about the vehicle acceleration and velocity. For that reason, time-series is synonymous with large amounts of data.


How Will Be The AI Job Market Fare In Coming Years?

#artificialintelligence

Artificial intelligence (AI) has quite evolved itself over recent decades. While it sparked numerous innovations and brought digital disruption to many industries, it also changed the jobs market forever. It is true that AI has replaced some jobs, however, it has also brought new roles for human workers. As per a 2017 research from IDC, released by Salesforce, AI is projected to create 823,734 jobs by the year 2021, surpassing the number of jobs lost to AI technologies such as machine learning and automation. The report had also predicted that AI would increase global business revenues by US$1.1 trillion in the same time frame.


AI-Enhanced Business Models for Digital Entrepreneurship

#artificialintelligence

The world of AI offers new opportunities for companies and is therefore of particular interest to entrepreneurs at potentially every level impacting their business. The following article therefore tries to classify the roles of artificial intelligence (AI) applications on the strategic level and their influence on business models. By means of case studies, current business practice will be examined to give entrepreneurs and researchers an understanding of this technology, by providing practical examples so that they can pursue their own AI path. The analysis is based on case studies that examine the role of AI in a company's business model, both for new market participants in the form of start-ups and incumbents such as the tech giants. By means of case studies, both sides of the extremes are covered in order to provide a picture of the scope of the applications.


Smart - Connected Women Partnership: Fast-Tracking Digital Fluency In The New Normal, Reinventing Jobs For Women

#artificialintelligence

To help boost women empowerment in the new normal, PLDT wireless unit Smart Communications, Inc. (Smart) has partnered with social impact start-up Connected Women for technology upskilling and livelihood opportunities for women across the Philippines. With the goal of training over a thousand women by 2021, Connected Women's Elevate AIDA (Artificial Intelligence Data Annotation) program offers online skills development and remote work opportunities in the artificial intelligence industry. Backed by UN Women, the 75,000 member-strong organization launched ConnectedWomen.ai to provide a talent pool for businesses worldwide while creating an impact for Filipino women and their families. Smart will support Connected Women's upskilling initiatives that include data labeling, remote work, professional communication, and computer skills, which are all scalable in the digital remote workspace. Participants will also benefit from career coaching, developing critical thinking and problem-solving skills and mentoring.


JaidedAI/EasyOCR

#artificialintelligence

Ready-to-use OCR with 70 languages supported including Chinese, Japanese, Korean and Thai. We are currently supporting 70 languages. See list of supported languages. Note 1: for Windows, please install torch and torchvision first by following the official instruction here https://pytorch.org. On pytorch website, be sure to select the right CUDA version you have.


Language & Cognition: re-reading Jerry Fodor

#artificialintelligence

In my opinion the late Jerry Fodor was one of the most brilliant cognitive scientists (that I knew of), if you wanted to have a deep understanding of the major issues in cognition and the plausibility/implausibility of various cognitive architectures. Very few had the technical breadth and depth in tackling some of the biggest questions concerning the mind, language, computation, the nature of concepts, innateness, ontology, etc. The other day I felt like re-reading his Concepts -- Where Cognitive Science Went Wrong (I read this small monograph at least 10 times before, and I must say that I still do not comprehend everything that's in it fully). But, what did happen in the 11th reading of Concepts is this: I now have a new and deeper understanding of his Productivity, Systematicity and Compositionality arguments that should clearly put an end to any talk of connectionist architectures being a serious architecture for cognition -- by'connectionist architectures' I roughly mean also modern day'deep neural networks' (DNNs) that are essentially, if we strip out the advances in compute power, the same models that were the target of Fodor's onslaught. I have always understood the'gist' of his argument, but I believe I now have a deeper understanding -- and, in the process I am now more than I have ever been before, convinced that DNNs cannot be considered as serious models for high-level cognitive tasks (planning, reasoning, language understanding, problem solving, etc.) beyond being statistical pattern recognizers (although very good ones at that).


Enhancing Humans with AI bots - discover.bot

#artificialintelligence

Artificial intelligence (AI) has both surpassed and replaced humans in many fields. Will AI overpower humanity in the near or distant future? Will AI control humans and replace governments? Or will AI remain a tool that humans will use to improve their performances? Research on brain-computer interface (BCI) has begun and suggests that we implant chips or connect devices to our brain to increase computing power.


2021 Complete Computer Vision Bootcamp, Zero-Hero in Python

#artificialintelligence

This Course is will teach you Computer Vision and Image Processing Techniques From Basic to Advance Level. This Course Provide all high quality content to learn and become Industry level Expert. We worked Really hard to explain the concepts of Computer Vision and Image Processing and the necessary mathematics behind each concept. You will get a Clear Idea about how computer understand and work with images and video Data. We will Start with a Short Python course where you will learn to code in python and will have clear understanding of python syntax and some advance concepts like python generators along with Object Oriented Programming.


Gift yourself a break from cleaning with a robot vacuum on sale

Mashable

TL;DR: Keep your home spotless with a Cybovac E30 robot vacuum cleaner for $189.97, a 23% savings as of Nov. 24. There's nothing quite as satisfying as sitting on the couch while a robot vacuum cleans up for you. This Cybovac E30 robot vacuum has been on sale before, but now it's at an even lower price. You won't even need to enter a coupon code. The Cybovac E30 has loads of great features, including smart navigation, the ability to set cleaning zones, and an impressive 150-minute cleaning time on a single charge.


Paving The Way For Software 2.0 With Kotlin - Liwaiwai

#artificialintelligence

Our work with differentiable programming, which enables programs to optimize themselves, is part of Facebook AI's broader efforts to build more advanced tools for machine learning (ML) programming. That's why we're extending the Kotlin compiler to make differentiability a first-class feature of the Kotlin language, as well as developing a system for tensor typing. Our work enables developers to explore Software 2.0, where software essentially writes itself, via: By enabling intuitive and performant differentiable programming in Kotlin, we're empowering developers to create powerful, flexible programs that take advantage of problem structure while seamlessly maintaining type safety and keeping debugging simple. Today, most code is either learnable (written using restrictive machine learning libraries) or explicitly programmed (using traditional coding paradigms). A major obstacle toward achieving Software 2.0 is that there's no true compatibility between these two methods.