Lifeboat Foundation

Brendan John Frey FRSC (born 29 August 1968) is a Canadian-born machine learning and genome biology researcher, known mainly for his work on factor graphs, the wake-sleep algorithm for deep learning, and using machine learning to model genome biology and understand genetic disorders. He founded Deep Genomics and is currently its CEO, and he is a Professor of Engineering and Medicine at the University of Toronto. He co-developed a new computational approach to identifying the genetic determinants of disease, was one of the first researchers to successfully train a deep neural network, and was a pioneer in the introduction of iterative message-passing algorithms.

Frey studied computer engineering and physics at the University of Calgary (BSc 1990) and the University of Manitoba (MSc 1993), and then studied neural networks and graphical models as a doctoral candidate at the University of Toronto under the supervision of Geoffrey Hinton (PhD 1997). He was an invited participant of the Machine Learning program at the Isaac Newton Institute for Mathematical Sciences in Cambridge, UK (1997) and was a Beckman Fellow at the University of Illinois at Urbana Champaign (1999).

Continue reading “Brendan Frey — Genome Reprogramming” »

When Kumar lost his job, he became part of a wave of layoffs washing through the Indian IT industry—a term that includes, in its vastness, call centers, engineering services, business process outsourcing firms, and infrastructure management and software companies. The recent layoffs are part of the industry’s most significant period of churn since it began to boom two decades ago. Companies don’t necessarily attribute these layoffs directly to automation, but at the same time, they constantly identify automation as the spark for huge changes in the industry. Bots, machine learning, and algorithms that robotically execute processes are rendering old skills redundant, recasting the idea of work and making a smaller labor force seem likely.

Technology outsourcing has been India’s only reliable job creator in the past 30 years. Now artificial intelligence threatens to wipe out those gains.

With machine learning algorithms evolving at an incredibly fast pace, concerns are mounting whether artificial intelligence (AI) is the logical continuation of human history or its demise. RT talked to three experts in the field about the benefits and dangers of AI.

Three AI experts engaged in a debate on RT about the benefits and dangers of rapidly-developing technology and AI.

Stanford researchers have developed an algorithm that offers diagnoses based off chest X-ray images. It can diagnose up to 14 types of medical conditions and is able to diagnose pneumonia better than expert radiologists working alone.

A paper about the algorithm, called CheXNet, was published Nov. 14 on the open-access, scientific preprint website arXiv.

“Interpreting X-ray images to diagnose pathologies like pneumonia is very challenging, and we know that there’s a lot of variability in the diagnoses radiologists arrive at,” said Pranav Rajpurkar, a graduate student in the Machine Learning Group at Stanford and co-lead author of the paper. “We became interested in developing machine learning algorithms that could learn from hundreds of thousands of chest X-ray diagnoses and make accurate diagnoses.”

In the mid-1900s, art historian Maurits Michel van Dantzig developed a system to identify artists by their brush or pen strokes, which he called Pictology. Dantzig found shape, length, direction, and pressure all contributed to a kind of stroke signature, unique to each artist.

New research with contributions from The Hague suggests that Pictology might be the key to helping machines understand art, allowing systems to quickly verify whether brushstrokes were from an original painter or a forger.

After analyzing 80,000 brushstrokes from 297 digitized sketches and drawings, an AI system was able to spot forged drawings in the style of Pablo Picasso, Henri Matisse, and Egon Schiele with 100% accuracy. The “fakes” were commissioned recreations of specific drawings, which the algorithms had not been shown previously.

Continue reading “This AI algorithm probably means the end of high-end art forgeries” »

Of course, it’s not actually all that doom and gloom, the child AI is really only capable of a specific task – image recognition. Using its AutoML AI, Google’s AI-building AI created its child AI using a technique called reinforcement learning. This works just like machine learning, except it’s entirely automated where AutoML acts as the neural network for its task-driven AI child.

Known as NASNet, the child AI was tasked with recognising objects in a video, in real time. AutoML would then evaluate how good NASNet was at its task and then improve its algorithms using the data to create a superior version of NASNet.

READ NEXT: Watching an AI create fake celebrity faces is nightmare fuel.

Continue reading “Google’s AI builds its own AI child and it’s better than anything humans have made” »

And it is on Singles’ Day, automation, robots, AI and machine learning will be widely applied to all aspects of the annual shopping ritual, right from product selection to delivery.

This year’s November 11 shopping ritual will engage a recommendation algorithm, robots, and chatbots capable of understanding human emotion.

Thank a new approach to spoofing image recognition AIs, developed by a team from Kyushu University in Japan, for that joke.

Trying to catch out AIs is a popular pastime for many researchers, and we’ve reported machine-learning spoofs in the past. The general approach is to add features to images that will incorrectly trigger a neural network and have it identify what it sees as something else entirely.

The new research, published on the arXiv, describes an algorithm that can efficiently identify the best pixels to alter in order to confuse an AI into mislabeling a picture. By changing just one pixel in a 1,024-pixel image, the software can trick an AI about 74 percent of the time. That figure rises to around 87 percent if five pixels are tweaked.

Continue reading “How Do You Turn a Dog into a Car? Change a Single Pixel” »

Google unveiled software aimed at making it easier for scientists to use the quantum computers in a move designed to give a boost to the nascent industry.

The software, which is open-source and free to use, could be used by chemists and material scientists to adapt algorithms and equations to run on quantum computers. It comes at a time when Google, IBM, Intel Corp., Microsoft Corp. and D-Wave Systems Inc. are all pushing to create quantum computers that can be used for commercial applications.