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.

Last month, DeepMind published the much anticipated, detailed methodology underlying the latest version of AlphaFold – the UK-based science company's powerful AI system that blew away its rivals in the latest major competition to predict the 3D structure of proteins. AlphaFold's machine learning methodology has been applied to predict structures for almost 99% of human proteins which have now been made publicly available. In this long read, I reflect on the significance of these developments for fundamental research and drug discovery. I wrote this as the ICR celebrates the 10th anniversary of its AI-enabled drug discovery knowledgebase canSAR – which features multiple approaches to predicting'druggability' as an aid to selecting drug targets and accelerating drug discovery. The coronavirus pandemic has, understandably, soaked up a lot of bandwidth when it comes to science news – but one particular non-Covid science story was able to cut through and hit the headlines in the UK and around the world. On 30 November 2020 it was announced that DeepMind – a subsidiary of Google's parent company Alphabet focusing on artificial intelligence – had made what was hailed as a huge leap towards solving one of biology's greatest remaining challenges: the ability to predict the correct, three-dimensional structures of proteins based on their constituent, one-dimensional amino acid sequences. The announcement attracted huge interest, but the expert community has been waiting for the peer-reviewed science publication. The AI methodology has now been published in the leading journal Nature and this was followed rapidly by a second Nature paper from DeepMind and collaborators at the European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), which reports the application of the most recent AlphaFold machine learning system to predict the 3D structures at scale for almost the entire human proteome – 98.5% of human proteins.

In Free Guy, the new film from director Shawn Levy and screenwriters Matt Lieberman and Zak Penn, Ryan Reynolds plays an NPC--a non-playable character--from a video game. Over the course of the movie, Reynolds' character becomes conscious, falls in love, and eventually learns how to use lightsabers, Captain America's shield, and a grab-bag of intellectual property controlled or licensed for the movie by the Walt Disney Company. It's not the first time NPCs have been featured on-screen, but no other film has spent as much time exploring their inner lives. To find out how NPCs felt about their moment in the spotlight, I travelled to several video game universes and asked any NPC I could find what they thought about Free Guy. Here's what they told me.