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Fun AI Apps Are Everywhere Right Now. But a Safety 'Reckoning' Is Coming

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If you've spent any time on Twitter lately, you may have seen a viral black-and-white image depicting Jar Jar Binks at the Nuremberg Trials, or a courtroom sketch of Snoop Dogg being sued by Snoopy. These surreal creations are the products of Dall-E Mini, a popular web app that creates images on demand. Type in a prompt, and it will rapidly produce a handful of cartoon images depicting whatever you've asked for. More than 200,000 people are now using Dall-E Mini every day, its creator says--a number that is only growing. A Twitter account called "Weird Dall-E Generations," created in February, has more than 890,000 followers at the time of publication.


Three opportunities of Digital Transformation: AI, IoT and Blockchain

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Koomey's law This law posits that the energy efficiency of computation doubles roughly every one-and-a-half years (see Figure 1–7). In other words, the energy necessary for the same amount of computation halves in that time span. To visualize the exponential impact this has, consider the face that a fully charged MacBook Air, when applying the energy efficiency of computation of 1992, would completely drain its battery in a mere 1.5 seconds. According to Koomey's law, the energy requirements for computation in embedded devices is shrinking to the point that harvesting the required energy from ambient sources like solar power and thermal energy should suffice to power the computation necessary in many applications. Metcalfe's law This law has nothing to do with chips, but all to do with connectivity. Formulated by Robert Metcalfe as he invented Ethernet, the law essentially states that the value of a network increases exponentially with regard to the number of its nodes (see Figure 1–8).


Has artificial intelligence (AI) come alive like in sci-fi movies? This Google engineer thinks so

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If you have ever interacted with a chatbot you know we're still years away from those things convincing you that you are chatting with a real human. That's no surprise as many chatbots do not actually use machine learning to converse more naturally. Instead only completing scripted actions based on keywords. A good chatbot that truly utilises machine learning can fool you into thinking that you're talking to a human. In fact, a program from 1965 fooled people into thinking that it was a human.


Protecting computer vision from adversarial attacks

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Advances in computer vision and machine learning have made it possible for a wide range of technologies to perform sophisticated tasks with little or no human supervision. From autonomous drones and self-driving cars to medical imaging and product manufacturing, many computer applications and robots use visual information to make critical decisions. Cities increasingly rely on these automated technologies for public safety and infrastructure maintenance. However, compared to humans, computers see with a kind of tunnel vision that leaves them vulnerable to attacks with potentially catastrophic results. For example, a human driver, seeing graffiti covering a stop sign, will still recognize it and stop the car at an intersection.


Swaayatt Robots: Pioneering Reinforcement Learning in Autonomous Driving

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The startup focuses on developing self-driving technology for unstructured environment conditions and India's road network is full of such environments. In the thick of it is founder and CEO Sanjeev Sharma, whose interest in the field of robotics was born way back in 2009, when he watched the videos of Team MIT at the 2007 DARPA Urban Challenge. With time, he knew that he wanted to hone in on research to enable autonomous driving in the most difficult traffic environmental scenarios, but it wasn't until 2014, when Sharma deferred his PhD at the University of Massachusetts for a year, that he established Swaayatt Robots. Fast forward eight years and, despite knowing much more about autonomous mobility than in 2014, safety continues to be a huge challenge. Even before we think of the purchasing and operational cost, we're quite some time away from solving for driver safety in an uncontrolled and unstructured environment -- but Swaayatt Robots is trying to fix that.


Intuit: Credit Karma And Mailchimp Integration A Game Changer (NASDAQ:INTU)

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Many of us are familiar with Intuit's (NASDAQ:INTU) industry-leading products in personal taxes (Turbo Tax) and small business accounting (QuickBooks). However, the company has expanded well beyond these two areas and assembled a portfolio of products that have improved and will continue to improve the financial lives of its customers. On Intuit's website, CEO Sasan Goodarzi described their mission statement as follows: We are a purpose-driven, values-driven company. Our mission to power prosperity around the world is why we show up to work every single day to do incredible things for our customers. Our values guide us and define what we stand for as a company.


7 Lessons I've Learnt From Deploying Machine Learning Models Using ONNX

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In this post, we will outline key learnings from a real-world example of running inference on a sci-kit learn model using the ONNX Runtime API in an AWS Lambda function. This is not a tutorial but rather a guide focusing on useful tips, points to consider, and quirks that may save you some head-scratching! The Open Neural Network Exchange (ONNX) format is a bit like dipping your french fries into a milkshake; it shouldn't work but it just does. ONNX allows us to build a model using all the training frameworks we know and love like PyTorch and TensorFlow and package it up in a format supported by many hardware architectures and operating systems. The ONNX Runtime is a simple API that is cross-platform and provides optimal performance to run inference on an ONNX model exactly where you need them: the cloud, mobile, an IoT device, you name it!


A Beginner's Guide to Basic Python

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Python programming language is a recommended language for beginners to learn. The reason is, this language has concise commands, so it is very easy to understand and write. You want to learn Python, but are confused about where to start? This time, we invite you to get to know what Python is, its functions and data types, to practice Python tutorials themselves. Python is one of the many examples of programming languages in the world.


Global Big Data Conference

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Has artificial intelligence finally come to life, or has it simply become smart enough to trick us into believing it has gained consciousness? Google engineer Blake Lemoine's recent claim that the company's AI technology has become sentient has sparked debate in technology, ethics and philosophy circles over if, or when, AI might come to life -- as well as deeper questions about what it means to be alive. Lemoine had spent months testing Google's chatbot generator, known as LaMDA (short for Language Model for Dialogue Applications), and grew convinced it had taken on a life of its own, as LaMDA talked about its needs, ideas, fears and rights. Google dismissed Lemoine's view that LaMDA had become sentient, placing him on paid administrative leave earlier this month -- days before his claims were published by The Washington Post. Most experts believe it's unlikely that LaMDA or any other AI is close to consciousness, though they don't rule out the possibility that technology could get there in future. "My view is that [Lemoine] was taken in by an illusion," Gary Marcus, a cognitive scientist and author of Rebooting AI, told CBC's Front Burner podcast.


Pinaki Laskar on LinkedIn: #AI #machinelearning #algorithms

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AI Researcher, Cognitive Technologist Inventor - AI Thinking, Think Chain Innovator - AIOT, XAI, Autonomous Cars, IIOT Founder Fisheyebox Spatial Computing Savant, Transformative Leader, Industry X.0 Practitioner How can a mathematically-oriented machine truly learn things? Mathematical machines are either formal logical systems, operationalized as symbolic rules-based AI or expert systems, or statistical learning machines, dubbed as narrow/Weak AI, ML, DL, ANNs. Such machines follow blind and mindless mathematical and statistical algorithms, codes, models, programs, and solutions, transforming input data (as independent variables) into the output data (as dependent variables), dubbed as predictions, recommendations, decisions, etc. They are unable to real knowing or learning, as having no interactions with the world, its various domains, rules, laws, objects, events, or processes. Learning is the "acquiring new understanding, knowledge, behaviors, skills, values, attitudes, and preferences" via senses, experience, trial and error, intuition, study and research.