Lifeboat Foundation

Last year, MIT developed an AI/ML algorithm capable of learning and adapting to new information while on the job, not just during its initial training phase. These “liquid” neural networks (in the Bruce Lee sense) literally play 4D chess — their models requiring time-series data to operate — which makes them ideal for use in time-sensitive tasks like pacemaker monitoring, weather forecasting, investment forecasting, or autonomous vehicle navigation. But, the problem is that data throughput has become a bottleneck, and scaling these systems has become prohibitively expensive, computationally speaking.

On Tuesday, MIT researchers announced that they have devised a solution to that restriction, not by widening the data pipeline but by solving a differential equation that has stumped mathematicians since 1907. Specifically, the team solved, “the differential equation behind the interaction of two neurons through synapses… to unlock a new type of fast and efficient artificial intelligence algorithms.”

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Physical dynamical processes can be modelled with differential equations that may be solved with numerical approaches, but this is computationally costly as the processes grow in complexity. In a new approach, dynamical processes are modelled with closed-form continuous-depth artificial neural networks. Improved efficiency in training and inference is demonstrated on various sequence modelling tasks including human action recognition and steering in autonomous driving.

For more than half a century, researchers around the world have been struggling with an algorithmic problem known as “the single source shortest path problem.” The problem is essentially about how to devise a mathematical recipe that best finds the shortest route between a node and all other nodes in a network, where there may be connections with negative weights.

Sound complicated? Possibly. But in fact, this type of calculation is already used in a wide range of the apps and technologies that we depend upon for finding our ways around—as Google Maps guides us across landscapes and through cities, for example.

Now, researchers from the University of Copenhagen’s Department of Computer Science have succeeded in solving the single source shortest path problem, a riddle that has stumped researchers and experts for decades.

Artificial Intelligence (AI) is rapidly changing the world. Emerging technologies on a daily basis in AI capabilities have lead to a number of innovations including autonomous vehicles, self-driving flights, robotics, etc. Some of the AI technologies feature predictions on future and accurate decision-making. AI is the best friend to technology leaders who want to make the world a better place with unfolding inventions.

Whether humans agree or not, AI developments are slowly impacting all aspects of the society including the economy. However, some technologies might even bring challenges and risks to the working environment. To keep a track on AI development, good leaders head the AI world to ensure trust, reliability, safety and accuracy.

Intelligent behaviour has long been considered a uniquely human attribute. But when computer science and IT networks started evolving, artificial intelligence and people who stood by them were on the spotlight. AI in today’s world is both developing and under control. Without a transformation here, AI will never fully deliver the problems and dilemmas of business only with data and algorithms. Wise leaders do not only create and capture vital economic values, rather build a more sustainable and legitimate organisation. Leaders in AI sectors have eyes to see AI decisions and ears to hear employees perspective.

“You can think of curiosity as a kind of reward which the agent generates internally on its own, so that it can go explore more about its world,” Agrawal said. This internally generated reward signal is known in cognitive psychology as “intrinsic motivation.” The feeling you may have vicariously experienced while reading the game-play description above — an urge to reveal more of whatever’s waiting just out of sight, or just beyond your reach, just to see what happens — that’s intrinsic motivation.

Humans also respond to extrinsic motivations, which originate in the environment. Examples of these include everything from the salary you receive at work to a demand delivered at gunpoint. Computer scientists apply a similar approach called reinforcement learning to train their algorithms: The software gets “points” when it performs a desired task, while penalties follow unwanted behavior.

An energy-efficient chip called NeuRRAM fixes an old design flaw to run large-scale AI algorithms on smaller devices, reaching the same accuracy as wasteful digital computers.

A new idea is helping to explain the puzzling success of today’s artificial-intelligence algorithms — and might also explain how human brains learn.

The number of AI and, in particular, machine learning (ML) publications related to medical imaging has increased dramatically in recent years. A current PubMed search using the Mesh keywords “artificial intelligence” and “radiology” yielded 5,369 papers in 2021, more than five times the results found in 2011. ML models are constantly being developed to improve healthcare efficiency and outcomes, from classification to semantic segmentation, object detection, and image generation. Numerous published reports in diagnostic radiology, for example, indicate that ML models have the capability to perform as good as or even better than medical experts in specific tasks, such as anomaly detection and pathology screening.

It is thus undeniable that, when used correctly, AI can assist radiologists and drastically reduce their labor. Despite the growing interest in developing ML models for medical imaging, significant challenges can limit such models’ practical applications or even predispose them to substantial bias. Data scarcity and data imbalance are two of these challenges. On the one hand, medical imaging datasets are frequently much more minor than natural photograph datasets such as ImageNet, and pooling institutional datasets or making them public may be impossible due to patient privacy concerns. On the other hand, even the medical imaging datasets that data scientists have access to could be more balanced.

In other words, the volume of medical imaging data for patients with specific pathologies is significantly lower than for patients with common pathologies or healthy people. Using insufficiently large or imbalanced datasets to train or evaluate a machine learning model may result in systemic biases in model performance. Synthetic image generation is one of the primary strategies to combat data scarcity and data imbalance, in addition to the public release of deidentified medical imaging datasets and the endorsement of strategies such as federated learning, enabling machine learning (ML) model development on multi-institutional datasets without data sharing.

A new quantum random-access memory device reads and writes information using a chirped electromagnetic pulse and a superconducting resonator, making it significantly more hardware-efficient than previous devices.

Random-access memory (or RAM) is an integral part of a computer, acting as a short-term memory bank from which information can be quickly recalled. Applications on your phone or computer use RAM so that you can switch between tasks in the blink of an eye. Researchers working on building future quantum computers hope that such systems might one day operate with analogous quantum RAM elements, which they envision could speed up the execution of a quantum algorithm [1, 2] or increase the density of information storable in a quantum processor. Now James O’Sullivan of the London Centre for Nanotechnology and colleagues have taken an important step toward making quantum RAM a reality, demonstrating a hardware-efficient approach that uses chirped microwave pulses to store and retrieve quantum information in atomic spins [3].

Just like quantum computers, experimental demonstrations of quantum memory devices are in their early days. One leading chip-based platform for quantum computation uses circuits made from superconducting metals. In this system, the central processing is done with superconducting qubits, which send and receive information via microwave photons. At present, however, there exists no quantum memory device that can reliably store these photons for long times. Luckily, scientists have a few ideas.