The US government wants to get hold of a quantum computer good enough to contribute to scientific breakthroughs in just two years. It will use it to try to accelerate the research and development of new materials, pharmaceuticals and molecules useful in agriculture and manufacturing. Once a dream of theoretical physicists, quantum computers are now undoubtedly real, but have yet to prove unambiguously useful or to have broad commercial value. Their computational power depends on their size - how many components called qubits they comprise - and how reliable they are. Existing devices are still too small and too error-prone.

Aprominent challenge is to train an agent capable of generalising to new environments attest time, rather than one that simply memorises trajectories and visual details observed during training. We propose a new learning strategy that learns both from observations and generatedcounterfactual environments.

Classes, as fundamental elements of Computer Vision, have been extensively studied within incremental learning frameworks. In contrast, tokens, which play essential roles in many research fields, exhibit similar characteristics of growth, yet investigations into their incremental learning remain significantly scarce. This research gap primarily stems from the holistic nature of tokens in language, which imposes significant challenges on the design of incremental learning frameworks for them. To overcome this obstacle, in this work, we turn to a type of token, gene, for a large-scale biological dataset--single-cell transcriptomics--to formulate a pipeline for gene incremental learning and establish corresponding evaluations. We found that the forgetting problem also exists in gene incremental learning, thus we adapted existing class incremental learning methods to mitigate the forgetting of genes. Through extensive experiments, we demonstrated the soundness of our framework design and evaluations, as well as the effectiveness of our method adaptations. Finally, we provide a complete benchmark for gene incremental learning in single-cell transcriptomics.

ImagenAI, a startup using AI to help professional photographers edit photos and automate post-production work, today announced that it raised $30 million in an all-equity growth investment from Summit Partners. The new capital brings Imagen's total raised to $34 million, and co-founder and CEO Yotam Gil tells TechCrunch that it'll be used to expand the startup's software-as-a-service offering through mergers and acquisitions and product research and development. Imagen's success comes as investors grow increasingly bullish on AI tools for generating and editing artwork, including photorealistic art. Cupixel, whose AI tech takes images to create outlines of the photo for drawings or paintings, recently raised $5 million. Meanwhile, Runway ML, which is developing an AI-powered creative suite for artists and which was a major research contributor to the text-to-image AI Stable Diffusion, landed $50 million in early December.

But IBM is aiming to steadily build that computing power in the years ahead. The company said it plans to introduce a more than 4,000-qubit system in 2025, which would be able to solve some problems faster or more accurately than classical computers, as well as provide exact solutions to problems the best of today's computers can only estimate, achieving a milestone known as "quantum advantage." The Morning Download delivers daily insights and news on business technology from the CIO Journal team. Dario Gil, senior vice president at IBM and director of IBM Research, said the company would continue scaling up from there, and ultimately quantum systems will contain millions of qubits. "We're getting closer and closer," said Dr. Gil. "This is another step.

Machine Learning (ML) is a type of artificial intelligence (AI) that allows software applications to become more accurate at predicting outcomes without being explicitly programmed. Machine learning algorithms use historical data as input to predict new output values. Technical Debt describes what results when development teams take conscious actions to expedite the delivery of a piece of functionality or a project which later needs to be remediated via refactoring. In other words, prioritizing speedy delivery over perfect code is the result. This article will present a simple yet powerful Model of Technical Debt for Machine Learning Systems.

For the past decade in Los Angeles and the State of California, the question is not if there will be wildfires--but rather when and where they will sprout up and how to protect people from these threats. As such, firefighters need to know how to plan and deploy limited resources. One such solution is controlled burns of flammable brush to prevent worst-case scenarios of growing tinder that left unattended, provides fodder for megafires. With $5 million in support from the National Science Foundation's Convergence Accelerator program, a team of researchers, which includes UC San Diego's San Diego Supercomputer Center (SDSC), the University of Southern California's Viterbi School of Engineering and the Tall Timbers Research Station in Florida, will bring the power of AI to help firefighters strategize how best to plan these controlled burns, as well as manage unexpected blazes. SDSC will lead the effort through the development of "BurnPro3D," a new decision support platform to help the fire response and mitigation community quickly and accurately understand risks and tradeoffs presented by a fire to more effectively plan controlled burns and manage wildfires.

Many companies are accelerating their cloud plans right now, and most say that their cloud usage will exceed prior estimates due to the new demands posed by the global pandemic. Cloud computing is becoming a must-have resource, especially for young tech companies. And most of them are migrating to Amazon, Google, or Azure, lured by seemingly attractive offers. What many companies don't realize is how dramatically the cloud spend can increase given that those expenses aren't charged up-front. Organizations are often unaware of how easy it is to become locked into service at hard-to-understand prices, says Laurent Gil, co-founder and Chief Product Officer at CAST AI. "Vendor lock-in starts whenever you start using a service in a way that serves the purpose of the cloud provider," he explains.

Quantum computing isn't yet far enough along that it could have helped curb the spread of this coronavirus outbreak. But this emerging field of computing will almost certainly help scientists and researchers confront future crises. "Can we compress the rate at which we discover, for example, a treatment or an approach to this?" asks Dario Gil, the director of IBM Research. "The goal is to do everything that we are doing today in terms of discovery of materials, chemistry, things like that, (in) factors of 10 times better, 100 times better," And that, he says, "could be game-changing." Quantum computing is the next big thing in computing, and it promises exponential advances in artificial intelligence and machine learning through the next decade and beyond, leading to potential breakthroughs in healthcare and pharmaceuticals, fertilizers, battery power, and financial services.

VentureBeat recently interviewed five of the most intelligent, expert minds in the AI field and asked them to make their predictions for where AI is heading over the course of the year to come. Chintala, the creator of Pytorch, which is arguably the most popular machine learning framework at the moment, predicted that 2020 will see a greater need for neural network hardware accelerators and methods of boosting model training speeds. Chintala expected that the next couple of years will see an increased focus on how to use GPUs optimally and how compiling can be done automatically for new hardware. Beyond this, Chintala expected that the AI community will begin pursuing other methods of quantifying AI performance more aggressively, placing less importance on pure accuracy. Factors for consideration include things like the amount of energy needed to train a model, how AI can be used to build the sort of society we want, and how the output of a network can be intuitively explained to human operators.