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) …
The "Curly" curling robots are capturing hearts around the world. A product of Korea University in Seoul and the Berlin Institute of Technology, the deep reinforcement learning powered bots slide stones along ice in a winter sport that dates to the 16th century. As much as their human-expert-bettering accuracy or technology impresses, a big part of the Curly appeal is how we see the little machines in the physical space: the determined manner in which the thrower advances in the arena, smartly raising its head-like cameras to survey the shiny white curling sheet, gently cradling and rotating a rock to begin delivery, releasing deftly at the hog line as a skip watches from the backline, with our hopes. Artificial intelligence (AI) today delivers everything from soup recipes to stock predictions, but most tech works out-of-sight. More visible are the physical robots of various shapes, sizes and functions that embody the latest AI technologies. These robots have generally been helpful, and now they are also becoming a more entertaining and enjoyable part of our lives.
Loads of research came out this week! But FYI, we couldn't fit every story in this newsletter for space-saving reasons, so if you want complete coverage, follow our twitter, and as always, if you enjoy the read, please give it a and share with your enemies. And….yesterday, another update was made to the Super Duper NLP Repo and the Big Bad NLP Database: we added 10 datasets and 5 new notebooks. Highlights include the DialogRE dataset which may be the first human-annotated dialogue-based relation extraction dataset. Legend has it there's a bitcoin wallet worth $690 million that hackers have been attempting to crack for the past 2 years according to cybersecurity expert Alon Gal.
Before Covid-19 financial institutions saw a 10:1 ratio of bot-based malicious to legitimate login attempts, according to Aite Group's Fraud & AML practice. Malicious login attempts are setting new records every month. Between 2018 and 2019, there was an 84% increase in the number of breached data reports, reaching 15.1B accounts last year. Fraud operations funded by organized crime run much like legitimate businesses, complete with ongoing recruiting campaigns for AI, bot and machine learning expertise and office locations focused on developing breach strategies. As of June 2020, login credentials for online banking averaged about $35 on the dark web while payment card details averaged between $12 and $20 apiece, according to analysis again by Help Net Security.
How AI can support small businesses and self-employed individuals during the pandemic? As the COVID-19 pandemic plays out around the world, consumers, small businesses, self-employed workers and accountants face unprecedented challenges, and these challenges only continue to grow. Many people are struggling to make ends meet and provide for their families. They might be facing a loss of income, a lack of adequate savings to weather the storm or poor access to health care. With shelter-in-place mandates proliferating around the world, small businesses have had to close their doors and are running out of cash to pay their employees and their bills.
I recently started a new newsletter focus on AI education. TheSequence is a no-BS( meaning no hype, no news etc) AI-focused newsletter that takes 5 minutes to read. The goal is to keep you up to date with machine learning projects, research papers and concepts. Humans build knowledge in images. Every time we are presented with an idea or an experience, our brain immediately formulates visual representations of it.
Machine Learning is an important technology for handling data in today's world. It is used to derive models of reality from data. For example, you can use it to segment customer data in an online store or to optimize a performance marketing campaign. This usually requires the use of a programming language with a large number of program libraries for the selected language. Very often "Python" or "R" are used here today and libraries like "Scikit Learn" and "TensorFlow".
In the first part of our tutorial on neural networks, we explained the basic concepts about neural networks, from the math behind them to implementing neural networks in Python without any hidden layers. We showed how to make satisfactory predictions even in case scenarios where we did not use any hidden layers. However, there are several limitations to single-layer neural networks. In this tutorial, we will dive in-depth on the limitations and advantages of using neural networks in machine learning. We will show how to implement neural nets with hidden layers and how these lead to a higher accuracy rate on our predictions, along with implementation samples in Python on Google Colab.
Truly, I think I have offered enough of my content and expertise for free. If you would like to access my centralized code repos, video tutorials, pre-configured Jupyter Notebooks in Google Colab, I have to ask that you kindly join PyImageSearch Plus. Your support of PyImageSearch Plus will not only give you a great education (which will pay off multiple times over), but you'll also be supporting myself/PyImageSearch, which allows me to produce even more high-quality content.
All students joining the Institute of Technical Education (ITE) from this year must take a module in the basics of artificial intelligence (AI) in their first year of study.The institute, which sees about 14,000 enrolling each year, is making a big push to teach them skills like basic programming and analytics, to meet the demands of emerging jobs. Please subscribe or log in to continue reading the full article.
Machine vision is commonly defined as the use of computer vision in the context of an industrial application, and the first use of machine vision for industrial purposes is often attributed to Electric Sorting Machine Company in the 1930s. They used a type of vacuum tube called a photomultiplier or PMT to sort food. Using this technology, machines could sort red apples from green and later recyclable glass bottles from ones with cracks. Much of the history of machine vision in the industrial sector has involved sorting one thing from another, the good from the bad. As camera technologies have improved, machine vision has been deployed for ever more precise quality control use cases, especially ones that involve parts that would be too small or hazardous for human inspectors.