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AI Lab of the Future Seeks to Build a Digital Cell

#artificialintelligence

The Technische Universität (TUU) KIWI biolab, which has been designated as one of three international artificial intelligence (AI) future laboratories by the German government, uses AI to design experiments with the aim of understanding how cells behave. "We cultivate various clones in parallel, and computer-controlled robots perform the fed-batch experiments and analyses automatically," explains Peter Neubauer, PhD, who heads the department of bioprocess engineering at TU Berlin. Experimental data is used to create "digital twins" of the cells that can be used for computer-based process development, he says. Neubauer developed the automated laboratory to multiply the number of cell lines he could analyze in parallel. "Currently in our facility we can do this for a large number of cells–for 48 different clones," he adds.


Everyone will be able to clone their voice in the future

#artificialintelligence

Cloning your voice using artificial intelligence is simultaneously tedious and simple: hallmarks of a technology that's just about mature and ready to go public. All you need to do is talk into a microphone for 30 minutes or so, reading a script as carefully as you can (in my case: the voiceover from a David Attenborough documentary). After starting and stopping dozens of times to re-record your flubs and mumbles, you'll send off the resulting audio files to be processed and, in a few hours' time, be told that a copy of your voice is ready and waiting. Then, you can type anything you want into a chatbox, and your AI clone will say it back to you, with the resulting audio realistic to fool even friends and family -- at least for a few moments. The fact that such a service even exists may be news to many, and I don't believe we've begun to fully consider the impact easy access to this technology will have.


Top 7 Artificial Intelligence Apps

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Smartphones equipped with artificial intelligence apps take note of our usage data and behavior patterns to make suggestions and automate mundane tasks. Imagine you left your smartphone at home for a day. Can you even think of going through a day without your smartphone? An ever-increasing number of people would say "No." That's how dependent we've become on smartphone technology to deliver the latest in news, entertainment, education, communication, image enhancement, and more.


Machine Learning Helps Clarify the Risk Connected to Age-Related Blood Condition

#artificialintelligence

Artificial intelligence (AI) and machine learning allow researchers to study databases that otherwise would be too large and complex. In a recent study, Sloan Kettering Institute computational biologist Quaid Morris and collaborators used models to study an aging-related blood condition called clonal hematopoiesis (CH). Their research showed how evolution and natural selection influence CH and the effects that it may have on health outcomes. CH is relatively common in older people, affecting up to 10% of the population by age 80. The condition raises the risk of developing blood disorders -- including some blood cancers -- and cardiovascular disease. "One of the issues that we face in studying something complicated like CH is the interplay of many different factors," says Dr. Morris, who is co-senior author of a paper on CH published August 13, 2021, in Nature Communications.


Step by step guide to training Detectron2 detection models on GPU -Part 1

#artificialintelligence

Part 1- The first part is about setting up the docker container for detectron2. The architecture of the detection model is a Faster region proposal convolutional neural network (FRCNN) with a Feature pyramid network(FPN) and the backbone is resnet101. We will learn the steps to train a multiclass model. Detectron2 is created by the Facebook research team. This is the official GitHub repository of Detectron2. It is a library that has algorithms written with a research perspective that deliver state of the art solutions to artificial intelligence -- computer vision focused problem statements.


New Anthony Bourdain documentary deepfakes his voice

#artificialintelligence

In a new documentary, Roadrunner, about the life and tragic death of Anthony Bourdain, there are a few lines of dialogue in Bourdain's voice that he might not have ever said out loud. Filmmaker Morgan Neville used AI technology to digitally re-create Anthony Bourdain's voice and have the software synthesize the audio of three quotes from the late chef and television host, Neville told the New Yorker. The deepfaked voice was discovered when the New Yorker's Helen Rosner asked how the filmmaker got a clip of Bourdain's voice reading an email he had sent to a friend. Neville said he had contacted an AI company and supplied it with a dozen hours of Bourdain speaking. " ... and my life is sort of shit now. You are successful, and I am successful, and I'm wondering: Are you happy?" Bourdain wrote in an email, and an AI algorithm later narrated an approximation of his voice.


TikTok Lawsuit Highlights How AI Is Screwing Over Voice Actors - Slashdot

#artificialintelligence

An anonymous reader quotes a report from Motherboard: With only 30 minutes of audio, companies can now create a digital clone of your voice and make it say words you never said. Using machine learning, voice AI companies like VocaliD can create synthetic voices from a person's recorded speech -- adopting unique qualities like speaking rhythm, pronunciation of consonants and vowels, and intonation. For tech companies, the ability to generate any sentence with a realistic-sounding human voice is an exciting, cost-saving frontier. But for the voice actors whose recordings form the foundation of text-to-speech (TTS) voices, this technology threatens to disrupt their livelihoods, raising questions about fair compensation and human agency in the age of AI. At the center of this reckoning is voice actress Bev Standing, who is suing TikTok after alleging the company used her voice for its text-to-speech feature without compensation or consent.


Representation mitosis in wide neural networks

arXiv.org Machine Learning

Deep neural networks (DNNs) defy the classical bias-variance trade-off: adding parameters to a DNN that exactly interpolates its training data will typically improve its generalisation performance. Explaining the mechanism behind the benefit of such over-parameterisation is an outstanding challenge for deep learning theory. Here, we study the last layer representation of various deep architectures such as Wide-ResNets for image classification and find evidence for an underlying mechanism that we call *representation mitosis*: if the last hidden representation is wide enough, its neurons tend to split into groups which carry identical information, and differ from each other only by a statistically independent noise. Like in a mitosis process, the number of such groups, or ``clones'', increases linearly with the width of the layer, but only if the width is above a critical value. We show that a key ingredient to activate mitosis is continuing the training process until the training error is zero. Finally, we show that in one of the learning tasks we considered, a wide model with several automatically developed clones performs significantly better than a deep ensemble based on architectures in which the last layer has the same size as the clones.


DeepCheapFakes

#artificialintelligence

Back in 2019, Ben Lorica and I wrote about deepfakes. Ben and I argued (in agreement with The Grugq and others in the infosec community) that the real danger wasn't "Deep Fakes." The real danger is cheap fakes, fakes that can be produced quickly, easily, in bulk, and at virtually no cost. Tactically, it makes little sense to spend money and time on expensive AI when people can be fooled in bulk much more cheaply. I don't know if The Grugq has changed his thinking, but there was an obvious problem with that argument.


Veritone launches new platform to let celebrities and influencers clone their voice with AI

#artificialintelligence

Recording advertisements and product endorsements can be lucrative work for celebrities and influencers. But is it too much like hard work? That's what US firm Veritone is betting. Today, the company is launching a new platform called Marvel.AI that will let creators, media figures, and others generate deepfake clones of their voice to license as they wish. "People want to do these deals but they don't have enough time to go into a studio and produce the content," Veritone president Ryan Steelberg tells The Verge.