Goto

Collaborating Authors

How to create an AI that chats like you on WhatsApp

#artificialintelligence

To train a GPT-2 neural network, first of all we need to pre-process the data, in order to obtain a single .txt For the sake of simplicity and since the machine learning model we will use requires a GPU to work, we're going to use Google Colab for the next step. If you don't know what Google Colab is, check this other article here. To work with the data, we need to upload them on Colab, into the right folders. Now, run all the cells up until the block "2 Parse the data".


Artificial Intelligence as a Service Industry Market Share Worldwide Industry Growth, Size, Statistics …

#artificialintelligence

In the latest report on'Artificial Intelligence as a Service Industry Market', added by Market Study Report, LLC, a concise analysis on the recent …


AI Reportedly Matches Tumors to Best Drug Combinations

#artificialintelligence

University of California San Diego School of Medicine and Moores Cancer Center say they have created a new artificial intelligence (AI) system called …


How To Solve a Rubik's Cube by Using Algorithms

#artificialintelligence

Love it or hate it, The Rubik's cube is one of the world's most popular puzzles. For many, it is an intimidating challenge, but it doesn't need to be. With a few simple algorithms and some perseverance, you too can solve one in short order if you haven't before. Read on to find out how. RELATED: HOW TO CHEAT AND MAKE IT LOOK LIKE YOU CAN SOLVE A RUBIK'S CUBE If you have trouble solving a Rubik's cube, don't fret, you are not alone.


Artificial Intelligence

#artificialintelligence

Traffic is that constant in our busy city lives that we hate but can't control. But, our dearest tech friend, AI is here to sort that out too. City administrators around the world are now working with artificial intelligence for smarter traffic control solutions. Artificial Intelligence based traffic management systems usually collect and analyze traffic data and patterns to provide solutions for traffic control. The field is still in its early phases of development. Thus, its implementation in the real world has been quite restricted so far.


Forrester: AI and automation will help organizations rethink the future of work

#artificialintelligence

A lasting legacy of the COVID-19 pandemic is that work will never be the same. Thanks to the two As, automation and artificial intelligence (AI), expect to see significant growth and changes in 2021. "The'great lockdown' of 2020 will make the drive for automation in 2021 both inevitable and irreversible,'' according to Forrester's Predictions 2021. "Remote work, new digital muscles, and pandemic constraints will create millions of pragmatic automations in 2021; document extraction, RPA (robotic process automation) from anywhere, drones, and various employee robots will proliferate; and, as expected, the mad dash to automate will bring trouble." At the same time, while AI didn't predict the pandemic, it will help businesses rethink the future of work; drive more efficiency, elasticity, and scale in operations; and reimagine customer and employee experiences, Forrester said. AI is driving the growth of automated processes, helping them become smarter. Companies that adopt machine learning, a subset of AI, "will massively multiply their number of AI use cases, including for employee augmentation and automation,'' the firm said.


Understanding satellite images: a data mining module for Sentinel images

#artificialintelligence

The Copernicus Access Platform Intermediate Layers Small Scale Demonstrator (CANDELA) project is a European Horizon 2020 research and innovation project for easy interactive analysis of satellite images on a web platform. Among its objectives are the development of efficient data retrieval and image mining methods augmented with machine learning techniques as well as interoperability capabilities in order to fully benefit from the available assets, the creation of additional value, and subsequently economic growth and development in the European member states (Candela, 2019). The potential target groups of users of the CANDELA platform are: space industries and data professionals, data scientists, end users (e.g., governmental and local authorities), and researchers in the areas covered by the project use cases (e.g., urban expansion and agriculture, forest and vineyard monitoring, and assessment of natural disasters) (Candela, 2019). In our case, this activity will generate a large geographical and temporal volume of EO data to be ingested into the data analytics building blocks. Activity 2: Tools for the fusion of various multi-sensor Earth observation satellite data (comprising, besides Sentinel, also several other contributing missions) with in-situ data and additional information from the web such as social networks or Open Data, in order to pave the way for new applications and services.


How surgical robots can shorten waiting lists – Innovation Origins – IAM Network

#artificialintelligence

How will robots change the world? A frequently asked and as yet unanswered question. After all, we do not have a crystal ball. What we do know is that digitalization and automation have changed the world enormously in recent decades. At Eindhoven University of Technology (TU/e) in the Netherlands, the potential of smart machines in industry and daily life is being researched each and every day.


Automate Image-based Inspection With sentin VISION System – IAM Network

#artificialintelligence

High demands on products as well as high time and cost pressure are decisive competitive factors across all industries and sectors. Whether in the food or automotive industry quality, safety and speed are today more than ever before factors that determine the success of a company. Zero-defect production is the goal. But how can it be guaranteed that only flawless products leave the production line? How can faulty quality decisions, which lead to high costs, be avoided?


Deep Learning Prerequisites: Linear Regression in Python

#artificialintelligence

Online Courses Udemy Data science: Learn linear regression from scratch and build your own working program in Python for data analysis. Created by Lazy Programmer Inc. English [Auto-generated], Spanish [Auto-generated] Students also bought Artificial Intelligence: Reinforcement Learning in Python Data Science: Natural Language Processing (NLP) in Python Natural Language Processing with Deep Learning in Python Cluster Analysis and Unsupervised Machine Learning in Python Complete Python Bootcamp: Go from zero to hero in Python 3 Preview this course GET COUPON CODE Description This course teaches you about one popular technique used in machine learning, data science and statistics: linear regression. We cover the theory from the ground up: derivation of the solution, and applications to real-world problems. We show you how one might code their own linear regression module in Python. Linear regression is the simplest machine learning model you can learn, yet there is so much depth that you'll be returning to it for years to come.