Lifeboat Foundation

In this age of “big data,” artificial intelligence (AI) has become a valuable ally for scientists. Machine learning algorithms, for instance, are helping biologists make sense of the dizzying number of molecular signals that control how genes function. But as new algorithms are developed to analyze even more data, they also become more complex and more difficult to interpret. Quantitative biologists Justin B. Kinney and Ammar Tareen have a strategy to design advanced machine learning algorithms that are easier for biologists to understand.

The algorithms are a type of artificial neural network (ANN). Inspired by the way neurons connect and branch in the brain, ANNs are the computational foundations for advanced machine learning. And despite their name, ANNs are not exclusively used to study brains.

Biologists, like Tareen and Kinney, use ANNs to analyze data from an experimental method called a “massively parallel reporter assay” (MPRA) which investigates DNA. Using this data, quantitative biologists can make ANNs that predict which molecules control specific genes in a process called gene regulation.

Even ordinary computers flip a bit here and there, but their quantum cousins have a lot more ways to go wrong.

As the power and qubits in quantum computing systems increase, so does the need for cutting-edge capabilities to ascertain that they work. The Army Research Office and National Security Agency recently teamed up to solicit proposals for research that can help do exactly that.

The entities launched a broad agency announcement this week to boost the development of innovative techniques and protocols that allow for Quantum Characterization, Verification, and Validation, or QCVV, of intermediate-scale quantum systems. QCVV is essentially the science of quantifying how well a quantum computer can run quantum algorithms—and experts agree that it’s a necessary step towards useful quantum computing.

Times reporters traveled to Russia, Switzerland, California and Washington, D.C., talking to experts in the commercial tech, military and A.I. communities. Below are some key points and analysis, along with extras from the documentary.

“Killing in the Age of Algorithms” is a New York Times documentary examining the future of artificial intelligence and warfare.

Nowadays, there is an imperative need for novel computational concepts to manage the enormous data volume produced by contemporary information technologies. The inherent capability of the brain to cope with these kinds of signals constitutes the most efficient computational paradigm for biomimicry.

Representing neuronal processing with software-based artificial neural networks is a popular approach with tremendous impacts on everyday life; a field commonly known as machine learning or artificial intelligence. This approach relies on executing algorithms that represent neural networks on a traditional von Neumann computer architecture.

An alternative approach is the direct emulation of the workings of the brain with actual electronic devices/circuits. This emulation of the brain at the hardware-based level is not only necessary for overcoming limitations of conventional silicon technology based on the traditional von Neumann architecture in terms of scaling and efficiency, but in understanding brain function through reverse engineering. This hardware-based approach constitutes the main scope of neuromorphic devices/computing.

This paper attempts to apply artificial intelligence (AI) to computer network technology and research on the application of AI in computing network technology.

With the continuous expansion of the application scope of computer network technology, various malicious attacks that exist in the Internet range have caused serious harm to computer users and network resources.

By studying the attack principle, analyzing the characteristics of the attack method, extracting feature data, establishing feature sets, and using the agent technology as the supporting technology, the simulation experiment is used to prove the improvement effect of the system in terms of false alarm rate, convergence speed, and false-negative rate, the rate reached 86.7%. The results show that this fast algorithm reduces the training time of the network, reduces the network size, improves the classification performance, and improves the intrusion detection rate.

Marcos López de Prado has been at the forefront of machine learning innovation in finance. The New-York based Spaniard was the first-ever head of machine learning at AQR, one of the world’s largest investment management firms, before he left earlier this year to start his own firm, which sells machine learning expertise and algorithms to Wall Street.

Science, not speculation, is the right way to invest, a top expert tells TIME.

Disruption of certain DNA structures—called topologically associating domains, or TADs—is linked with the development of disease, including some cancers. With its newly created algorithm that quickly locates and helps elucidate the complex functions of TADs, an international team of researchers is making it easier to study these important structures and help prevent disease.

“On your DNA you have genes and regulatory elements—such as promotors and enhancers—that control gene expression, but these two things can be far away from each other,” said Qunhua Li, associate professor of statistics, Penn State. “Similar to a dresser drawer that keeps your clothes organized and available for use, TADs bring genes together with their regulatory elements, which enables them to begin the process of gene expression.”

Gene expression is the process by which the information encoded in DNA gives rise to observable traits.

Machine learning and AI can now restore lost detail to blurry images. Many companies and labs have created such “super resolution” algorithms, but this software is now becoming commercially available, too. Pixelmator Pro is among the first image editors to offer such a tool.

In 1965, American engineer Gordon Moore made the prediction that the number of transistors integrated on a silicon chip doubles every two years or so. This has proven to be true to this day, allowing software developers to double the performance of their applications. However, the performance of artificial intelligence (AI) algorithms seems to have outpaced Moore’s Law.

According to a new report produced by Stanford University, AI computational power is accelerating at a much higher rate than the development of processor chips.

“Prior to 2012, AI results closely tracked Moore’s Law, with compute doubling every two years,” the authors of the report wrote. “Post-2012, compute has been doubling every 3.4 months.”