A fragment of a bronze military diploma from Sardinia, issued by the emperor Trajan to a sailor on a warship, as restored by Aeneas. If you believe the hype, generative artificial intelligence (AI) is the future. However, new research suggests the technology may also improve our understanding of the past. A team of computer scientists from Google DeepMind, working with classicists and archaeologists from universities in the United Kingdom and Greece, described a new machine-learning system designed to help experts to understand ancient Latin inscriptions. Named Aeneas (after the mythical hero of Rome's foundation epic), the system is a generative neural network designed to provide context for Latin inscriptions written between the 7th century BCE and the 8th century CE.

The paper describes a family of multi-class SVMs parameterised by a single parameter p. The family contains the Crammer & Singer SVM as a special case. Corresponding generalization bounds are derived, which for certain parameter choices have a logarithmic dependency on the number of classes. The latter has - to my knowledge - not been shown for any multi-class classifier. The corresponding learning machine is derived including its Fenchel dual problem.

Power systems dominated by renewable energy encounter frequently large, random disturbances, and a critical challenge faced in power-system management is how to anticipate accurately whether the perturbed systems will return to the functional state after the transient or collapse. Whereas model-based studies show that the key to addressing the challenge lies in the attracting basins of the functional and dysfunctional states in the phase space, the finding of the attracting basins for realistic power systems remains a challenge, as accurate models describing the system dynamics are generally unavailable. Here we propose a new machine learning technique, namely balanced reservoir computing, to infer the attracting basins of a typical power system based on measured data. Specifically, trained by the time series of a handful of perturbation events, we demonstrate that the trained machine can predict accurately whether the system will return to the functional state in response to a large, random perturbation, thereby reconstructing the attracting basin of the functional state. The working mechanism of the new machine is analyzed, and it is revealed that the success of the new machine is attributed to the good balance between the echo and fading properties of the reservoir network; the effect of noisy signals on the prediction performance is also investigated, and a stochastic-resonance-like phenomenon is observed. Finally, we demonstrate that the new technique can be also utilized to infer the attracting basins of coexisting attractors in typical chaotic systems.

Video game designer Jason Allen made headlines this year with Théâtre D'opéra Spatial, his submission to the Colorado State Fair's digital arts competition. Judges awarded him first place and $300 prize, but the artwork also received a sudden flurry of global attention when it was discovered Allen had used AI-powered image generator Midjourney to create the work of art. Midjourney, DALL-E and DALL-E 2 have brought a wealth of weird and wonderful images to the world as users type in natural language descriptions and share the dream-like results. DALL-E 2 uses a "diffusion model", which attempts to take the input text in its entirety and generate an image from that. But the output becomes less accurate as that text becomes more complex; the existing model appears to struggle to understand composition of concepts, and confuses attributes and relations between different objects.

Scientists have developed a method using machine learning to better analyze data from a powerful scientific tool: nuclear magnetic resonance (NMR). One way NMR data can be used is to understand proteins and chemical reactions in the human body. NMR is closely related to magnetic resonance imaging (MRI) for medical diagnosis. NMR spectrometers allow scientists to characterize the structure of molecules, such as proteins, but it can take highly skilled human experts a significant amount of time to analyze that data. This new machine learning method can analyze the data much more quickly and just as accurately.

Toronto artificial intelligence (AI) startup Ada is bolstering its tech stack with a new machine learning centre in Israel and the appointment of a chief product officer (CPO). "The motivation to open the machine learning centre in Israel stems from the pool of talent there in conversational AI and in machine learning." This week, Ada announced the opening of its office in Israel, where it will be hiring machine learning, engineering, and product teams to continue to develop the conversational AI systems that power its automated brand interaction platform. Israel's growing AI market is what attracted Ada to make inroads into the country, according to the startup. Research firm Tracxn estimates that there are currently 1,100 startups in Israel that use AI as a core component of their offering.

You already know that Google uses machine learning algorithms to filter spam out of Gmail, to make Google Maps capable of the high level of accuracy it maintains, and more, but did you know that it's also now being used to detect whether or not a website's notification prompts are being abused? If you read our coverage just a few days ago, we covered this, but Google has now revealed that it's performing these ML predictions entirely on your device! This means that your data stays private and never leaves your Chromebook, phone or tablet, making Safe Browsing, well, safer than ever. With the next release of Chrome, The following is what you'll see if a phishing attempt is detected. This new ML algorithm is actually 2.5 times better at identifying potentially malicious sites and phishing attacks compared to the previous model.

How do cancer cells differ from healthy cells? A new machine learning algorithm called "ikarus" knows the answer, reports a team led by MDC bioinformatician Altuna Akalin in the journal Genome Biology. The AI program has found a gene signature characteristic of tumors. When it comes to identifying patterns in mountains of data, human beings are no match for artificial intelligence (AI). In particular, a branch of AI called machine learning is often used to find regularities in data sets – be it for stock market analysis, image and speech recognition, or the classification of cells.

Introduced last year, Vertex AI is a collection of cloud services for creating AI models. Some of the services in the platform are geared toward tech-savvy companies that build fully custom neural networks from scratch. Other Vertex AI components are designed to help developers with limited machine learning expertise create AI software more easily.

The biotech industry is constantly searching for the perfect mutation, where properties from different proteins are synthetically combined to achieve a desired effect. It may be necessary to develop new medicaments or enzymes that prolong the shelf-life of yogurt, break down plastics in the wild, or make washing powder effective at low water temperature. New research from DTU Compute and the Department of Computer Science at the University of Copenhagen (DIKU) can in the long term help the industry to accelerate the process. In the journal Nature Communications, the researchers explain how a new way of using Machine Learning (ML) draws a map of proteins, which makes it possible to appoint a candidate list of the proteins that you need to examine more closely. In recent years, we have started to use Machine Learning to form a picture of permitted mutations in proteins.