The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. The Unesco standards define a new category of data, 'neural data', and suggest guidelines governing its protection. Unesco adopts global standards on'wild west' field of neurotechnology UN body's recommendations driven by AI advances and proliferation of consumer-oriented neurotech devices It is the latest move in a growing international effort to put guardrails around a burgeoning frontier - technologies that harness data from the brain and nervous system. Unesco has adopted a set of global standards on the ethics of neurotechnology, a field that has been described as "a bit of a wild west". "There is no control," said Unesco's chief of bioethics, Dafna Feinholz.

The Nearest Neighbor model is the $\textit{de facto}$ thermodynamic model of RNA secondary structure formation and is a cornerstone of RNA structure prediction and sequence design. The current functional form (Turner 2004) contains $\approx13,000$ underlying thermodynamic parameters, and fitting these to both experimental and structural data is computationally challenging. Here, we leverage recent advances in $\textit{differentiable folding}$, a method for directly computing gradients of the RNA folding algorithms, to devise an efficient, scalable, and flexible means of parameter optimization that uses known RNA structures and thermodynamic experiments. Our method yields a significantly improved parameter set that outperforms existing baselines on all metrics, including an increase in the average predicted probability of ground-truth sequence-structure pairs for a single RNA family by over 23 orders of magnitude. Our framework provides a path towards drastically improved RNA models, enabling the flexible incorporation of new experimental data, definition of novel loss terms, large training sets, and even treatment as a module in larger deep learning pipelines. We make available a new database, RNAometer, with experimentally-determined stabilities for small RNA model systems.

Leading home improvement retailer Lowe's Companies, Inc. recently concluded an exciting and engaging webinar as part of their TechSprint series, in collaboration with Analytics India Magazine. Curated by Isaac Mathew, senior director, technology (DACI) and Swaroop Shivaram, director, data science, at Lowe's, the webinar focused on the company's capabilities and latest advancements in AI/ML, besides touching upon some of the challenges, solutions, work culture, and career opportunities. Impact delivered by Lowe's using AI/ML solutions Mathew began the webinar focusing on data analytics across retail operations. With businesses trying to provide a personalised experience with an omnichannel approach, Lowe's enhances customer experience through data assessment. "AI and ML have become very integral in all decision making. We help businesses make decisions in a way they can have intelligence embedded in all their products," Mathew said.

Visualized are two-dimensional pressure fluctuations within a larger three-dimensional magnetically confined fusion plasma simulation. With recent advances in machine-learning techniques, these types of partial observations provide new ways to test reduced turbulence models in both theory and experiment. MIT researchers are testing a simplified turbulence theory's ability to model complex plasma phenomena using a novel machine-learning technique. To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel. Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT's Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces -- factors that impact fusion reactor designs. To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior.

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel. Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT's Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces – factors that impact fusion reactor designs. To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior.

Tech workers Jared Parker and Patrick Dougherty first met three years ago when they "took data scientists out for lunch and dinners and just heard them complain about their current world," Parker told Insider. The pain point they heard again and again, according to Parker, was "Why am I spending all my time extracting, exploring, cleaning, joining, transforming raw data into a set of features that can be consumed by my model?" Their answer to that frustration is Rasgo Intelligence, a startup they founded a year ago during the height of the pandemic, that helps data scientists prep their data, reuse code, and ultimately build machine learning models much more efficiently. On Thursday, the New York-based startup raised a $20 million Series A led by Insight Partners with participation from Unusual Ventures. This latest round brings its total funding to over $25 million; the startup declined to disclose its valuation.

However, for many IT organizations, AI is not just on the IT leader's radar as a business enabler: It's having fundamental impacts on the function itself – from automating some longstanding functions to demanding greater involvement and newer approaches from IT teams. AI is beginning to reshape IT in a number of ways that forward-looking IT leaders will want to follow. Let's consider five worth watching: Tools to automate traditional break-fix and other IT service desk processes are not new, but they're getting significant traction these days, says Wayne Butterfield, director of cognitive automation and innovation at ISG. "An IT Service Desk is as prone to repetition (and therefore automation) as a customer service operation," he says. That's not the only area of hyper AI-enabled automation coming for the IT function. "IT has quickly become not just a partner but a consumer as well, leveraging AI for security and system management to automate processes and move at the speed of an AI-driven enterprise," says Shawn Rogers, vice president of analytic strategy at TIBCO.

In this paper, we propose an end-to-end deep learning model, called E2Efold, for RNA secondary structure prediction which can effectively take into account the inherent constraints in the problem. The key idea of E2Efold is to directly predict the RNA base-pairing matrix, and use an unrolled algorithm for constrained programming as the template for deep architectures to enforce constraints. With comprehensive experiments on benchmark datasets, we demonstrate the superior performance of E2Efold: it predicts significantly better structures compared to previous SOTA (especially for pseudoknotted structures), while being as efficient as the fastest algorithms in terms of inference time.

One of the holy grails in the world of advertising and marketing has been finding a way to accurately capture and understand what consumers are doing throughout the day, regardless of whether it's a digital or offline activity. That goal has become even more elusive in recent years, with the surge of regulations around privacy and data protection that limit what kind of information can be collected and used. Now, a startup believes it's cracked the code, and it's raised a large round of funding that underscores its success so far and what it believes is untapped future demand. Near, which has built an interactive, cloud-based AI platform called AllSpark that works across 44 countries to create anonymised, location-based profiles of users -- 1.6 billion each month at present -- based on a trove of information that it sources and then merges from phones, data partners, carriers and its customers, but which it claims was built "with privacy by design", has raised $100 million. The company believes that this Series C -- from a single backer, Great Pacific Capital out of London -- is one of the biggest rounds ever to be raised in this particular area of marketing technology.