On Monday, the leadership of the Screen Actors Guild-American Federation of Television and Radio Artists, or SAG-AFTRA, held a members-only webinar to discuss the contract the union tentatively agreed upon last week with the Alliance of Motion Picture and Television Producers (AMPTP). If ratified, the contract will officially end the longest labor strike in the guild's history. For many in the industry, artificial intelligence was one of the strike's most contentious, fear-inducing components. Over the weekend, SAG released details of their agreed AI terms, an expansive set of protections that require consent and compensation for all actors, regardless of status. With this agreement, SAG has gone substantially further than the Directors Guild of America (DGA) or the Writers Guild of America (WGA), who preceded them in coming to terms with the AMPTP.

I've been in the entertainment industry since I was nine. I joined the Screen Actors Guild (SAG) when I was 11 in 1977, the Writers Guild of America (WGA) when I was 22, and the Directors Guild of America (DGA) the following year. I got my start as a child actor on Broadway, studied film at NYU, then went on to act in movies like The Lost Boys and the Bill & Ted franchise while writing and directing my own narrative work. I've lived through several labor crises and strikes, but none like our current work shutdown, which began last spring when all three unions' contracts were simultaneously due for renegotiation and the Alliance of Motion Picture and Television Producers (AMPTP) refused their terms. The unifying stress point for labor is the devaluing of the worker, which reached a boiling point with the rapid advancement of highly sophisticated and ubiquitous machine learning tools. Actors have been replaced by AI replications of their likenesses, or their voices have been stolen outright.

To help combat climate change, many car manufacturers are racing to add more electric vehicles in their lineups. But to convince prospective buyers, manufacturers need to improve how far these cars can go on a single charge. Figuring out how to make extremely powerful but lightweight batteries. Typically, however, it takes decades for scientists to thoroughly test new battery materials, says Pablo Leon, an MIT graduate student in materials science. To accelerate this process, Leon is developing a machine-learning tool for scientists to automate one of the most time-consuming, yet key, steps in evaluating battery materials.

Just a few years ago, Nastacia Goodwin spent most days sitting at a computer in a lab at Smith College in Northampton, Massachusetts, stopwatch in hand, eyes fixed on three-hour long videos of prairie voles. Whenever an animal huddled close to another -- click -- she recorded the duration of their interaction. It didn't take long before Goodwin, now a graduate student in Sam Golden's research group at the University of Washington in Seattle, became eager to find a faster, less biased way to annotate videos. Machine learning was a logical choice. Goodwin co-developed Simple Behavioral Analysis, or SimBA, an open-source tool to automatically detect and classify animal behaviors from videos.

Terms like "machine learning," "artificial intelligence" and "deep learning" have all become science buzzwords in recent years. But can these technologies be applied to saving lives? The answer to that is a resounding yes. Future developments in health science may actually depend on integrating rapidly growing computing technologies and methods into medical practice. Cosmos spoke with researchers from the University of Pittsburgh, in Pennsylvania, US, who have just published a paper in Radiology on the use of machine-learning techniques to analyse large data sets from brain trauma patients.

Speaking at the 2022 Diabetes UK Professional Conference, Julia Townson (Cardiff University, UK) explained that around a quarter of children with type 1 diabetes in the UK are not diagnosed until they are in diabetic ketoacidosis (DKA), with rates unchanged for 25 years despite public health campaigns, highlighting the need for improved tools for early detection. She said that previous research identified different patterns of primary care contact among children who later go on to develop type 1 diabetes versus those who do not, leading the team to hypothesize that primary care data could be used to flag those likely to be diagnosed with the condition. To investigate this, Townson and colleagues used a machine-learning algorithm drawing on 81 pieces of information from electronic health records studied from 2000 to 2016 to produce a single score that indicates the likelihood of being diagnosed with type 1 diabetes. The information used in the tool included flags such as family history, fatigue, urinary tract infections, obesity, and weight loss, as well as data on the frequency of recent primary care contact relative to average contact frequency for each child. The Welsh SAIL/Brecon registry of approximately 35 million primary care contacts for 1 million children (0.21% with type 1 diabetes) was used as the training dataset, and the tool was tested using the English Clinical Practice Research Datalink (CPRD) and Hospital Episode Statistics records involving around 43 million contacts for 1.5 million children (0.10% with type 1 diabetes).

Oracle's gargantuan $28.3 billion acquisition of health care data company Cerner, the largest deal in its 44-year history, is not just about electronic patient records. From algorithmic systems that predict the likelihood a patient will contract sepsis to tech that tracks hospital bed capacity, Cerner will bring an array of cloud-based data analytics and AI technologies to Oracle as it competes with Amazon Web Services, Google, IBM and others to serve the health care industry's data and AI needs. In fact, the deal is poised to shift some business away from AWS, which Cerner named as its preferred cloud partner in 2019. Oracle's acquisition of Cerner, a company that got its start in health care IT in 1979, is expected to close in 2022. The all-cash deal is also expected to improve Oracle's bottom line in its first year, the company said in a press release.

"I didn't think we would get to this point in my lifetime." That's how one research leader in structural biology responded to last week's publication of research in which artificial intelligence (AI) was used to predict the structure of more than 20,000 human proteins, as well as that of nearly all the known proteins produced by 20 model organisms such as Escherichia coli, fruit flies and yeast, but also soya bean and Asian rice. That is a combined total of around 365,000 predictions1. The data, publicly accessible for the first time (see https://alphafold.ebi.ac.uk), were released online on 22 July by researchers at DeepMind, a London-based AI company owned by Google's parent company, Alphabet, and the European Bioinformatics Institute, based at the European Molecular Biology Laboratory (EBI-EMBL) near Cambridge, UK. DeepMind's AI predicts structures for a vast trove of proteins The DeepMind team developed a machine-learning tool called AlphaFold.

Like every facet of modern business, technology presses on its journey of transforming how we function and behave. Human Resources (HR) is one such department that despite containing the word "human" in it has benefitted from technological transform. From big data to Internet of Things, virtual and augmented reality, mobility, cloud computing, blockchain, and a legion of technologies, both emergent and evolving, have encroached into sophisticated HR domains in the industry. The technology that most strikes the eye in augmenting HR refinement and progression is Machine Learning (ML) coupled with Artificial Intelligence (AI). Historical data has increasingly come in handy when identifying trends and patterns.

Four of the newfound quadruply imaged quasars are shown here: From top left and moving clockwise, the objects are: GraL J1537-3010 or "Wolf's Paw;" GraL J0659 1629 or "Gemini's Crossbow;" GraL J1651-0417 or "Dragon's Kite;" GraL J2038-4008 or "Microscope Lens." The fuzzy dot in the middle of the images is the lensing galaxy, the gravity of which is splitting the light from the quasar behind it in such a way to produce four quasar images. By modeling these systems and monitoring how the different images vary in brightness over time, astronomers can determine the expansion rate of the universe and help solve cosmological problems. With the help of machine-learning techniques, a team of astronomers has discovered a dozen quasars that have been warped by a naturally occurring cosmic "lens" and split into four similar images. Quasars are extremely luminous cores of distant galaxies that are powered by supermassive black holes.