In 1972, Anfinsen won a Nobel prize for demonstrating a connection between a protein's amino acid sequence and its three-dimensional structure. Since 1994, scientists have competed in the biannual Critical Assessment of Structure Prediction (CASP) protein-folding challenge. Deep learning methods took center stage at CASP14, with DeepMind's Alphafold2 achieving remarkable accuracy. Baek et al. explored network architectures based on the DeepMind framework. They used a three-track network to process sequence, distance, and coordinate information simultaneously and achieved accuracies approaching those of DeepMind. The method, RoseTTA fold, can solve challenging x-ray crystallography and cryo–electron microscopy modeling problems and generate accurate models of protein-protein complexes. Science , abj8754, this issue p. [871][1] DeepMind presented notably accurate predictions at the recent 14th Critical Assessment of Structure Prediction (CASP14) conference. We explored network architectures that incorporate related ideas and obtained the best performance with a three-track network in which information at the one-dimensional (1D) sequence level, the 2D distance map level, and the 3D coordinate level is successively transformed and integrated. The three-track network produces structure predictions with accuracies approaching those of DeepMind in CASP14, enables the rapid solution of challenging x-ray crystallography and cryo–electron microscopy structure modeling problems, and provides insights into the functions of proteins of currently unknown structure. The network also enables rapid generation of accurate protein-protein complex models from sequence information alone, short-circuiting traditional approaches that require modeling of individual subunits followed by docking. We make the method available to the scientific community to speed biological research. [1]: /lookup/doi/10.1126/science.abj8754

Artificial intelligence (AI) and machine-learning (ML) have quickly grown beyond a few major tech companies and hardcore academic researchers. Every marketing organization can tap into the power of AI to streamline operations and grow the business. The new book The AI Marketing Canvas: A Five-Stage Road Map to Implementing Artificial Intelligence in Marketing provides a growth framework for business and marketing leaders to implement AI using a five-stage model called the "AI Marketing Canvas." On this episode of Marketing Smarts, I speak with co-author Rajkumar Venkatesan about how he and his co-writer developed those stages by studying leading global brands. We cover examples of brands―including Google, Lyft and Coca-Cola―that have successfully woven AI into their marketing strategies. This is not a conversation about coding for AI models. Raj and I talk about how marketing leaders can go from "zero to hero" with AI in marketing, and what that means for your team and your company culture. Listen to the entire show now from the link above, or download the mp3 and listen at your convenience.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Everyone is familiar with IBM's leadership in IT, AI, and cloud services. It also happens to be one of the leading providers for Enterprise Asset Management software through its Maximo line of software and services. These tools help manage large machines, like factories, powerplants, and heavy equipment. Now, with the rise of digital twins, IBM is pivoting this business as an onramp to bringing intelligence, agility, and efficiency to a wide range of industries.

The phrase "data science" is used every day, including in this very publication. We feel like we have an idea what it is. But what exactly is it? For one answer, we turn to Jeffrey Ullman, who won the Turing Award in 2020. "Where does data science come from?" asked Ullman, a Stanford University computer science professor, during his keynote address at the 27th ACM Special Interest Group on Knowledge Discovery and Data Mining (SIGKDD) conference on Monday.

As part of a three-month focus on data analysis MethodSpace is featuring original writings, podcasts, and videos on all phases of the process for both qualitative and quantitative studies. In this interview, learn about a free tool, SAGE Texti. Q: Hi Daniela, can you tell us a little about your background? I have a multi-disciplinary background, starting off in biochemistry, then moving into economics. I wrote my PhD in corporate innovation, while working in fintech to support the development of data crawlers and algorithms.

In the 1950s, the two British physiologists and biophysicists Alan Hodgkin and Andrew Huxley conducted a study of the giant axons in the neurons of the squid. The squid is convenient for neuroscience research because its axons are visible to the naked eye. The researchers measured the voltage across neurons, and were able to measure the voltages needed to excite a neuron and induce it to transmit a signal to its neighbors. The critical voltage is called the action potential. Later, together with their Australian colleague John Eccles, the team was awarded the 1963 Nobel Prize in Physiology for their work.

So there's the first bias, which is, if you are learning in isolation, the hospital is learning, a neural network model, or a machine learning model, more generally, of a hospital is learning in isolation only on their own private patient data, they will be naturally biased towards the demographics they are seeing. For example, we have an example where a hospital trains their machine learning models on chest x-rays and sees a lot of tuberculosis cases. But very little of lung collapsed cases. So therefore, this neural network model, when trained, will be very sensitive to what's detecting tuberculosis and less sensitive towards detecting lung collapse, for example. However, we get the converse of it in another hospital. So what you really want is to have these two hospitals combine their data so that the resulting neural network model can predict both situations better. But since you can't share that data, swarm learning comes in to help reduce that bias of both the hospitals.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Pepperdata, a provider of tools that optimize IT infrastructure for computation, has seen a lot of trends come and go over the years. Now organizations are using the company's tools to optimize infrastructure to process AI models. VentureBeat caught up with Pepperdata CEO Ash Munshi to gain a deeper appreciation for IT issues, such as data management, that are holding back the rate at which enterprise IT departments can meaningfully implement AI. But he also pointed out that a lot of companies struggling with AI might be fighting the wrong battle for their business needs.

A key part of any solution should involve revolutionizing data collection systems. We often don't have the data we need to build unbiased AI, whether it is the lack of uniformity in electronic health records, state-by-state variation in criminal sentence reporting, or missing data in the 2020 Census. The solution will require increased funding to agencies that gather data in underrepresented communities, outreach to community organizations to gain local buy-in, and democratization of data so that citizen scientists can highlight disparities and identify ongoing gaps.

AI increasingly is being used to make artwork, but experts disagree over whether computers truly can be creative. A new website even lets you try your hand at collaborating on art with AI. Artifly gets to know the user's preferences and creates artwork based on what they like. Not everyone thinks this process makes Artifly an artist, however. "Creativity is an innately human characteristic with the power to help us cope, connect, and be inspired," Scott Prevost, software firm Adobe's vice president of engineering, who focuses on AI technology, told Lifewire in an email interview.